Elamur Research⌕
☰
home / papers / 2022 / A Survey of Implicit Discourse Relation Recognition

A Survey of Implicit Discourse Relation Recognition

Paper · 2022 · Wei Xiang and Bang Wang

Summary

This survey covers implicit discourse relation recognition (IDRR). The task is to decide which logical relation holds between two adjacent text segments (arguments) when no connective such as "because" or "but" marks it. Recovering these hidden links matters for downstream work like question answering, summarization and machine translation. A causal link, for example, tells a system what answers a "why" question.

The authors build the task definition on the Penn Discourse TreeBank (PDTB) and the CoNLL shallow discourse parsing shared tasks. They sort existing solutions by development history into three families:

  • traditional machine learning with hand-built linguistic features,
  • deep neural models that learn representations end to end,
  • semi-supervised schemes that enlarge scarce labeled data.

They then compare reported results on PDTB under a standard data setup. Neural models lead, but the gap remains large. Relations with an explicit connective reach about 93% accuracy, while the best implicit-relation systems sit near 80%. The survey ends with three research directions: richer argument interaction, cleaner synthetic training data, and joint modeling with full discourse parsing.

Key points

  • PDTB annotation. PDTB annotates over one million words of Wall Street Journal text, on top of the Penn Treebank and Propbank. Versions are 1.0 (2006), 2.0 (2008) and 3.0 (2019, updated 2020 via LDC). Version 3.0 adds intra-sentential relations.
  • Sense hierarchy. Senses form three levels: class, type and subtype. The four top classes are Temporal, Comparison, Contingency and Expansion. One relation may carry more than one sense.
  • Implicit connectives. For implicit relations, PDTB annotators insert an "implicit connective" (e.g. "so") that expresses the relation they perceive.
  • CoNLL shared tasks. The CoNLL-2015 and CoNLL-2016 shared tasks on Shallow Discourse Parsing provide a second standard benchmark, limited to adjacent arguments.
  • Why IDRR is hard. Lin et al. name four causes of weak performance: ambiguity between relations, the need for inference, contextual modeling, and world knowledge.

Interesting ideas

  • I1. PDTB makes an implicit relation concrete by having annotators write in the connective they would have used, which turns a hidden relation into a checkable token.
  • I2. Training data for implicit relations can be manufactured by deleting explicit connectives, but such instances differ from naturally implicit ones, so they should not be treated as gold.
  • I3. A three-level sense hierarchy (class → type → subtype) that allows multiple senses per relation lets a system commit to coarse labels when unsure and refine them when evidence permits.
  • I4. PDTB keeps each argument span minimal and marks relevant-but-unnecessary text separately as supplementary material.
  • I5. The authors argue that relations are better judged against the whole discourse, using neighbouring arguments and their relations, rather than as isolated pairs.

For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

  • I1 (frames): between clause frames with no overt link, prag could hypothesize a latent "because/so/then" edge and store it with its supporting evidence, keeping the inference explainable.
  • I2 (learn): silver discourse-relation data can be mined from explicit connectives, while a held-out set of genuinely implicit cases serves as a control that measures the distribution shift.
  • I3 (world): event-ordering and causal links could be stored with hierarchical, possibly multi-valued labels (e.g. both temporal and causal for "since"), so readers can back off to the coarser class.
  • I4 (world): event and state spans could be kept minimal, with modifiers attached as side information, giving cleaner entities for timelines and holdings.
  • I5 (answer): readers answering "why" questions about fables could check a candidate causal link against surrounding links in the story graph before using it.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Huazhong University of Science and TechnologyWuhan · China17 works · 3,504 citations

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 9,086 worksArtificial Intelligence · 8,889 worksArts and Humanities · 9,296 worksLanguage and Linguistics · 9,206 works

🧭 Topics 1Research subjects and languages the work is about.Discourse analysis · 394 works

Cite

Cite as Wei Xiang and Bang Wang. 2022. A Survey of Implicit Discourse Relation Recognition. arXiv:2203.02982.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2203.02982 · arXiv 2203.02982

@misc{xiang2022survey,
  title = {{A Survey of Implicit Discourse Relation Recognition}},
  author = {Wei Xiang and Bang Wang},
  year = {2022},
  doi = {10.48550/arxiv.2203.02982},
  eprint = {2203.02982},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2203.02982}
}

References

175 references, 90 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. Mikel Artetxe and Holger Schwenk. 2019. Massively multilingual sentence embeddings for zero-shot cross-lingual transfer and beyond. Transactions of the Association for Computational Linguistics 7 (2019), 597–610. https://doi.org/10.1162/tacl_a_00288 ↗
  2. Katherine Atwell, Junyi Jessy Li, and Malihe Alikhani. 2021. Where Are We in Discourse Relation Recognition?. In Proceedings of the 22nd Annual Meeting of the Special Interest Group on Discourse and Dialogue (Singapore) (SIGDIAL ’21). The Association for Computational Linguistics, Stroudsburg, PA, USA, 314–325. https://aclanthology.org/2021.sigdial-1.34 ↗
  3. Hongxiao Bai and Hai Zhao. 2018. Deep Enhanced Representation for Implicit Discourse Relation Recognition. In Proceedings of the 27th International Conference on Computational Linguistics (Santa Fe, New Mexico, USA) (COLING ’18). The Association for Computational Linguistics, Stroudsburg, PA, USA, 571–583. https://www.aclweb.org/anthology/C18-1048/ ↗
  4. Yoshua Bengio, Réjean Ducharme, Pascal Vincent, and Christian Jauvin. 2003. A neural probabilistic language model. J. Mach. Learn. Res. 3, Feb (March 2003), 1137–1155.
  5. Or Biran and Kathleen McKeown. 2013. Aggregated Word Pair Features for Implicit Discourse Relation Disambiguation. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) (Sofia, Bulgaria) (ACL ’13). The Association for Computer Linguistics, Stroudsburg, PA, USA, 69–73. https://www.aclweb.org/anthology/P13-2013/ ↗
  6. Sasha Blair-Goldensohn, Kathleen McKeown, and Owen Rambow. 2007. Building and Refining Rhetorical-Semantic Relation Models. In Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Proceedings of the Main Conference (Rochester, New York, USA) (NAACL ’07). The Association for Computational Linguistics, Stroudsburg, PA, USA, 428–435. https://www.aclweb.org/anthology/N07-1054/ ↗
  7. Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, and Oksana Yakhnenko. 2013. Translating embeddings for modeling multi-relational data. In Advances in neural information processing systems (Lake Tahoe, Nevada) (NIPS’13, Vol. 26). Curran Associates Inc., Red Hook, NY, USA, 2787–2795. http://papers.nips.cc/paper/5071-translating-embeddings-for-modeling-multi-relational-data ↗
  8. Chloé Braud and Pascal Denis. 2014. Combining natural and artificial examples to improve implicit discourse relation identification. In Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers (Dublin, Ireland) (COLING ’14). The Association for Computational Linguistics, Stroudsburg, PA, USA, 1694–1705. https://www.aclweb.org/anthology/C14-1160/ ↗
  9. Chloé Braud and Pascal Denis. 2015. Comparing Word Representations for Implicit Discourse Relation Classification. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (Lisbon, Portugal) (EMNLP ’15). The Association for Computational Linguistics, Stroudsburg, PA, USA, 2201–2211. https://doi.org/10.18653/v1/d15-1262 ↗
  10. Chloé Braud and Pascal Denis. 2016. Learning Connective-based Word Representations for Implicit Discourse Relation Identification. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (Austin, Texas, USA) (EMNLP ’16). The Association for Computational Linguistics, Stroudsburg, PA, USA, 203–213. https://doi.org/10.18653/v1/d16-1020 ↗
  11. Peter F Brown, Vincent J Della Pietra, Peter V Desouza, Jennifer C Lai, and Robert L Mercer. 1992. Class-based n-gram models of natural language. Computational linguistics 18, 4 (Dec. 1992), 467–480. ↗
  12. Deng Cai and Hai Zhao. 2017. Pair-aware neural sentence modeling for implicit discourse relation classification. In International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (Arras, France) (Lecture Notes in Computer Science). Springer, Berlin, Germany, 458–466. https://doi.org/10.1007/978-3-319-60045-1_47 ↗
  13. Jifan Chen, Qi Zhang, Pengfei Liu, and Xuanjing Huang. 2016. Discourse relations detection via a mixed generative-discriminative framework. In Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (Phoenix, Arizona, USA) (AAAI ’16). AAAI Press, Palo Alto, California, USA, 2921–2927. http://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/view/11815 ↗
  14. Jifan Chen, Qi Zhang, Pengfei Liu, Xipeng Qiu, and Xuan-Jing Huang. 2016. Implicit discourse relation detection via a deep architecture with gated relevance network. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (Berlin, Germany) (ACL ’16). The Association for Computational Linguistics, Stroudsburg, PA, USA, 1726–1735. https://doi.org/10.18653/v1/p16-1163 ↗
  15. Christian Chiarcos and Niko Schenk. 2015. A minimalist approach to shallow discourse parsing and implicit relation recognition. In Proceedings of the Nineteenth Conference on Computational Natural Language Learning-Shared Task (Beijing, China) (CoNLL ’15). The Association for Computational Linguistics, Stroudsburg, PA, USA, 42–49. https://doi.org/10.18653/v1/k15-2006 ↗
  16. Philipp Cimiano, Uwe Reyle, and Jasmin Šarić. 2005. Ontology-driven discourse analysis for information extraction. Data & Knowledge Engineering 55, 1 (2005), 59 – 83. https://doi.org/10.1016/j.datak.2004.11.009 Natural Language and Database and Information Systems. ↗
  17. Ronan Collobert and Jason Weston. 2008. A unified architecture for natural language processing: Deep neural networks with multitask learning. In Proceedings of the 25th international conference on Machine learning (Helsinki, Finland) (ACM International Conference Proceeding Series). ACMPress, New York, NY, USA, 160–167. https://doi.org/10.1145/1390156.1390177 ↗
  18. Zeyu Dai and Ruihong Huang. 2018. Improving Implicit Discourse Relation Classification by Modeling Interdependencies of Discourse Units in a Paragraph. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) (New Orleans, Louisiana, USA) (NAACL ’18). The Association for Computational Linguistics, Stroudsburg, PA, USA, 141–151. https://doi.org/10.18653/v1/n18-1013 ↗
  19. Sobha Lalitha Devi, Sindhuja Gopalan, S Lakshmi, Pattabhi RK Rao, Vijay Sundar Ram, and CS Malarkodi. 2015. A hybrid discourse relation parser in conll 2015. In Proceedings of the Nineteenth Conference on Computational Natural Language Learning-Shared Task (Beijing, China) (CoNLL ’15). The Association for Computational Linguistics, Stroudsburg, PA, USA, 50–55. https://doi.org/10.18653/v1/k15-2007 ↗
  20. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) (Minneapolis, MN, USA) (NAACL ’19). The Association for Computational Linguistics, Stroudsburg, PA, USA, 4171– 4186. https://doi.org/10.18653/v1/n19-1423 ↗
  21. Ziwei Fan, Min Zhang, and Zhenghua Li. 2019. BiLSTM-based Implicit Discourse Relation Classification Combing Self-attention Mechanism and Syntactic Information. Computer Science 46, 5 (2019), 214–220. https://doi.org/10.11896/j.issn.1002-137X.2019.05.033 ↗
  22. Robert Fisher and Reid Simmons. 2015. Spectral semi-supervised discourse relation classification. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers) (Beijing, China) (ACL ’15). The Association for Computational Linguistics, Stroudsburg, PA, USA, 89–93. https://doi.org/10.3115/v1/p15-2015 ↗
  23. Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, and Victor Lempitsky. 2016. Domain-adversarial training of neural networks. J. Mach. Learn. Res. 17, 1 (Jan. 2016), 2096–2030. ↗
  24. Ruiying Geng, Ping Jian, Yingxue Zhang, and Heyan Huang. 2017. Implicit discourse relation identification based on tree structure neural network. In 2017 International Conference on Asian Language Processing (Singapore) (IALP ’17). IEEE Press, New York, NY, USA, 334–337. https://doi.org/10.1109/IALP.2017.8300611 ↗
  25. Shima Gerani, Yashar Mehdad, Giuseppe Carenini, Raymond Ng, and Bita Nejat. 2014. Abstractive summarization of product reviews using discourse structure. In Proceedings of the 2014 conference on empirical methods in natural language processing (Doha, Qatar) (EMNLP ’14). The Association for Computational Linguistics, Stroudsburg, PA, USA, 1602–1613. https://doi.org/10.3115/v1/d14-1168 ↗
  26. Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. Generative adversarial nets. In Advances in neural information processing systems (Montreal, Quebec, Canada) (NeurIPS ’14). MIT Press, Cambridge, MA, USA, 2672–2680. http://papers.nips.cc/paper/5423-generative-adversarial-nets ↗
  27. Fengyu Guo, Ruifang He, and Jianwu Dang. 2019. Implicit Discourse Relation Recognition via a BiLSTM-CNN Architecture With Dynamic Chunk-Based Max Pooling. IEEE Access 7 (2019), 169281–169292. https://doi.org/10.1109/ACCESS.2019.2954988 ↗
  28. Fengyu Guo, Ruifang He, Jianwu Dang, and Jian Wang. 2020. Working Memory-Driven Neural Networks with a Novel Knowledge Enhancement Paradigm for Implicit Discourse Relation Recognition. In The Thirty-Fourth AAAI Conference on Artificial Intelligence (New York, NY, USA) (AAAI ’20). AAAI Press, Palo Alto, California, USA, 7822–7829. https://aaai.org/ojs/index.php/AAAI/article/view/6287 ↗
  29. Fengyu Guo, Ruifang He, Di Jin, Jianwu Dang, Longbiao Wang, and Xiangang Li. 2018. Implicit discourse relation recognition using neural tensor network with interactive attention and sparse learning. In Proceedings of the 27th International Conference on Computational Linguistics (Santa Fe, New Mexico, USA) (COLING ’18). The Association for Computational Linguistics, Stroudsburg, PA, USA, 547–558. https://www.aclweb.org/anthology/C18-1046/ ↗
  30. Francisco Guzmán, Shafiq Joty, Lluís Màrquez, and Preslav Nakov. 2014. Using discourse structure improves machine translation evaluation. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (Baltimore, MD, USA) (ACL ’04). The Association for Computer Linguistics, Stroudsburg, PA, USA, 687–698. https://doi.org/10.3115/v1/p14-1065 ↗
  31. Ruifang He, Jian Wang, Fengyu Guo, and Yugui Han. 2020. TransS-driven joint learning architecture for implicit discourse relation recognition. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Online) (ACL ’20). The Association for Computational Linguistics, Stroudsburg, PA, USA, 139–148. https://doi.org/10.18653/v1/2020.acl-main.14 ↗
  32. Hugo Hernault, Danushka Bollegala, and Mitsuru Ishizuka. 2010. A semi-supervised approach to improve classification of infrequent discourse relations using feature vector extension. In Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing (Massachusetts, USA) (EMNLP ’10). The Association for Computational Linguistics, Stroudsburg, PA, USA, 399–409. https://www.aclweb.org/anthology/D10-1039/ ↗
  33. Yu Hong, Siyuan Ding, Yang Xu, Xiaoxia Jiang, Yu Wang, Jianmin Yao, Qiaoming Zhu, and Guodong Zhou. 2019. Focus-sensitive relation disambiguation for implicit discourse relation detection. Frontiers of Computer Science 13, 6 (2019), 1266–1281. https://doi.org/10.1007/s11704-017-6558-y ↗
  34. Yu Hong, Xiaopei Zhou, Tingting Che, Jianmin Yao, Qiaoming Zhu, and Guodong Zhou. 2012. Cross-argument inference for implicit discourse relation recognition. In Proceedings of the 21st ACM international conference on Information and knowledge management (Maui, HI, USA) (CIKM ’12). ACMPress, New York, NY, USA, 295–304. https://doi.org/10.1145/2396761.2396801 ↗
  35. Chaowen Hu, Yalian Yang, and Changxing Wu. 2020. Survey of Implicit Discourse Relation Recognition Based on Deep Learning. Computer Science 47, 4 (2020), 157–163. http://www.jsjkx.com/CN/abstract/article_18980.shtml ↗
  36. Tommi Jaakkola and David Haussler. 1999. Exploiting generative models in discriminative classifiers. In Advances in neural information processing systems (Denver, Colorado, USA) (NeurIPS ’99). MIT Press, Cambridge, MA, USA, 487–493. http://papers.nips.cc/paper/1520-exploiting-generative-models-in-discriminative-classifiers ↗
  37. Peter Jansen, Mihai Surdeanu, and Peter Clark. 2014. Discourse complements lexical semantics for non-factoid answer reranking. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (Baltimore, MD, USA) (ACL ’14). The Association for Computer Linguistics, Stroudsburg, PA, USA, 977–986. https://doi.org/10.3115/v1/p14-1092 ↗
  38. Rodolphe Jenatton, Nicolas L Roux, Antoine Bordes, and Guillaume R Obozinski. 2012. A latent factor model for highly multi-relational data. In Advances in neural information processing systems (Lake Tahoe, Nevada, USA) (NeurIPS ’12). MIT Press, Cambridge, MA, USA, 3167–3175. http://papers.nips.cc/paper/4744-a-latent-factor-model-for-highly-multi-relational-data ↗
  39. Guoliang Ji, Shizhu He, Liheng Xu, Kang Liu, and Jun Zhao. 2015. Knowledge graph embedding via dynamic mapping matrix. In Proceedings of the 53rd annual meeting of the association for computational linguistics and the 7th international joint conference on natural language processing (volume 1: Long papers) (Beijing, China) (ACL ’15). The Association for Computational Linguistics, Stroudsburg, PA, USA, 687–696. https://doi.org/10.3115/v1/p15-1067 ↗
  40. Yangfeng Ji, Trevor Cohn, Lingpeng Kong, Chris Dyer, and Jacob Eisenstein. 2015. Document context language models. (2015). arXiv:arXiv:1511.03962 ↗
  41. Yangfeng Ji and Jacob Eisenstein. 2015. One Vector is Not Enough: Entity-Augmented Distributed Semantics for Discourse Relations. Transactions of the Association for Computational Linguistics 3 (2015), 329–344. https://doi.org/10.1162/tacl_a_00142 ↗
  42. Yangfeng Ji, Gholamreza Haffari, and Jacob Eisenstein. 2016. A Latent Variable Recurrent Neural Network for Discourse-Driven Language Models. In The 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (San Diego California, USA) (NAACL ’16). The Association for Computational Linguistics, Stroudsburg, PA, USA, 332–342. https://doi.org/10.18653/v1/n16-1037 ↗
  43. Yangfeng Ji, Gongbo Zhang, and Jacob Eisenstein. 2015. Closing the gap: Domain adaptation from explicit to implicit discourse relations. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (Lisbon, Portugal) (EMNLP ’15). The Association for Computational Linguistics, Stroudsburg, PA, USA, 2219–2224. https://doi.org/10.18653/v1/d15-1264 ↗
  44. Ping Jian, Xiaohan She, Chenwei Zhang, Pengcheng Zhang, and Jian Feng. 2016. Discourse relation sense classification systems for conll-2016 shared task. In Proceedings of the Twentieth Conference on Computational Natural Language Learning-Shared Task (Berlin, Germany) (CoNLL ’16). The Association for Computational Linguistics, Stroudsburg, PA, USA, 158–163. https://doi.org/10.18653/v1/k16-2022 ↗
  45. Dan Jiang and Jin He. 2020. Tree framework with BERT word embedding for the recognition of Chinese implicit discourse relations. IEEE Access 8 (2020), 162004–162011. https://doi.org/10.1109/ACCESS.2020.3019500 ↗
  46. Feng Jiang, Peifeng Li, and Qiaoming Zh. 2021. Recognizing Chinese Discourse Relations Based on Multi-Perspective and Hierarchical Modeling. In 2021 International Joint Conference on Neural Networks (Shenzhen, China) (IJCNN ’21). IEEE, IEEE Press, New York, NY, USA, 1–8. https://doi.org/10.1109/IJCNN52387.2021.9534273 ↗
  47. Yao jie Lu, Mu Xu, Chang xing Wu, De yi Xiong, Hong ji Wang, and Jin song Su. 2018. Cross-lingual implicit discourse relation recognition with co-training. Frontiers of Information Technology & Electronic Engineering 19, 5 (2018), 651–661. https://doi.org/10.1631/FITEE.1601865 ↗
  48. Nal Kalchbrenner, Edward Grefenstette, and Phil Blunsom. 2014. A convolutional neural network for modelling sentences. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Baltimore, MD, USA) (ACL ’14). The Association for Computational Linguistics, Stroudsburg, PA, USA, 655–665. https://doi.org/10.3115/v1/p14-1062 ↗
  49. Yusuke Kido and Akiko Aizawa. 2016. Discourse relation sense classification with two-step classifiers. In Proceedings of the Twentieth Conference on Computational Natural Language Learning-Shared Task (Berlin, Germany) (CoNLL ’16). The Association for Computational Linguistics, Stroudsburg, PA, USA, 129–135. https://doi.org/10.18653/v1/k16-2018 ↗
  50. Najoung Kim, Song Feng, R. Chulaka Gunasekara, and Luis A. Lastras. 2020. Implicit Discourse Relation Classification: We Need to Talk about Evaluation. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (Online) (ACL ’20). The Association for Computational Linguistics, Stroudsburg, PA, USA, 5404–5414. https://doi.org/10.18653/v1/2020.acl-main.480 ↗
  51. Yoon Kim. 2014. Convolutional Neural Networks for Sentence Classification. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (Doha, Qatar) (EMNLP ’14). The Association for Computational Linguistics, Stroudsburg, PA, USA, 1746–1751. https://doi.org/10.3115/v1/d14-1181 ↗
  52. Yoon Kim, Yacine Jernite, David Sontag, and Alexander M Rush. 2016. Character-aware neural language models. In Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (Phoenix, Arizona, USA) (AAAI ’16). AAAI Press, Palo Alto, California, USA, 2741–2749. http://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/view/12489 ↗
  53. Paul R. Kingsbury and Martha Palmer. 2002. From TreeBank to PropBank. In Proceedings of the Third International Conference on Language Resources and Evaluation (Las Palmas, Canary Islands, Spain) (LREC ’02). European Language Resources Association, Paris, France, 1989–1993. http://www.lrec-conf.org/proceedings/lrec2002/sumarios/283.htm ↗
  54. Yudai Kishimoto, Yugo Murawaki, and Sadao Kurohashi. 2018. A knowledge-augmented neural network model for implicit discourse relation classification. In Proceedings of the 27th International Conference on Computational Linguistics (Santa Fe, New Mexico, USA) (COLING ’2018). The Association for Computational Linguistics, Stroudsburg, PA, USA, 584–595. https://www.aclweb.org/anthology/C18-1049/ ↗
  55. Yudai Kishimoto, Yugo Murawaki, and Sadao Kurohashi. 2020. Adapting BERT to Implicit Discourse Relation Classification with a Focus on Discourse Connectives. In Proceedings of The 12th Language Resources and Evaluation Conference (Marseille, France) (LREC ’20). European Language Resources Association, Paris, France, 1152–1158. https://www.aclweb.org/anthology/2020.lrec-1.145/ ↗
  56. Murathan Kurfalı and Robert Ostling. 2019. Zero-shot transfer for implicit discourse relation classification. In 20th Annual Meeting of the Special Interest Group on Discourse and Dialogue (Stockholm, Sweden) (SIGDIAL ’19). The Association for Computational Linguistics, Stroudsburg, PA, USA, 226–231. https://doi.org/10.18653/v1/W19-5927 ↗
  57. Guillaume Lample, Miguel Ballesteros, Sandeep Subramanian, Kazuya Kawakami, and Chris Dyer. 2016. Neural architectures for named entity recognition. In The 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (San Diego California, USA) (NAACL ’16). The Association for Computational Linguistics, Stroudsburg, PA, USA, 260–270. https://doi.org/10.18653/v1/n16-1030 ↗
  58. Man Lan, Jianxiang Wang, Yuanbin Wu, Zheng-Yu Niu, and Haifeng Wang. 2017. Multi-task attention-based neural networks for implicit discourse relationship representation and identification. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (Copenhagen, Denmark) (EMNLP ’17). The Association for Computational Linguistics, Stroudsburg, PA, USA, 1299–1308. https://doi.org/10.18653/v1/d17-1134 ↗
  59. Man Lan, Yu Xu, and Zheng-Yu Niu. 2013. Leveraging synthetic discourse data via multi-task learning for implicit discourse relation recognition. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (Sofia, Bulgaria) (ACL ’13). The Association for Computational Linguistics, Stroudsburg, PA, USA, 476–485. https://www.aclweb.org/anthology/P13-1047/ ↗
  60. Alan Lee, Rashmi Prasad, Aravind Joshi, Nikhil Dinesh, and Bonnie Webber. 2006. Complexity of dependencies in discourse: Are dependencies in discourse more complex than in syntax. In Proceedings of the 5th International Workshop on Treebanks and Linguistic Theories (Prague, Czech Republic) (TLT ’06). University of Pennsylvania, Philadelphia, PA, USA, 12–23. https://www.seas.upenn.edu/~pdtb/papers/lee-etal-tlt06.pdf ↗
  61. Wenqiang Lei, Xuancong Wang, Meichun Liu, Ilija Ilievski, Xiangnan He, and Min-Yen Kan. 2017. SWIM: a simple word interaction model for implicit discourse relation recognition. In Proceedings of the 26th International Joint Conference on Artificial Intelligence (Melbourne, Australia) (IJCAI ’17). Elsevier, Amsterdam, Netherlands, 4026–4032. https://doi.org/10.24963/ijcai.2017/562 ↗
  62. Wenqiang Lei, Yuanxin Xiang, Yuwei Wang, Qian Zhong, Meichun Liu, and Min-Yen Kan. 2018. Linguistic Properties Matter for Implicit Discourse Relation Recognition: Combining Semantic Interaction, Topic Continuity and Attribution. In 32nd AAAI Conference on Artificial Intelligence (New Orleans, Louisiana, USA) (AAAI ’18). AAAI Press, Palo Alto, California, USA, 4848–4855. https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/17260 ↗
  63. Beth Levin. 1993. English verb classes and alternations: A preliminary investigation. University of Chicago press, Chicago, IL, USA. find
  64. Dejian Li, Man Lan, and Yuanbin Wu. 2019. Comparative Investigation of Deep Learning Components for End-to-end Implicit Discourse Relationship Parser. In China National Conference on Chinese Computational Linguistics (Kunming, China) (Lecture Notes in Computer Science, Vol. 11856). Springer, Berlin, Germany, 143–155. https://doi.org/10.1007/978-3-030-32381-3_12 ↗
  65. Huifeng Li, Rohini K Srihari, Cheng Niu, and Wei Li. 2002. Location normalization for information extraction. In Proceedings of the 19th international conference on Computational linguistics-Volume 1 (Taipei, Taiwan) (COLING ’02). The Association for Computational Linguistics, Stroudsburg, PA, USA, 1–7. https://www.aclweb.org/anthology/C02-1127/ ↗
  66. Haoran Li, Jiajun Zhang, Yu Zhou, and Chengqing Zong. 2016. Predicting implicit discourse relation with multi-view modeling and effective representation learning. In Natural Language Understanding and Intelligent Applications (Kunming, China) (Lecture Notes in Computer Science, Vol. 10102). Springer, Berlin, Germany, 374–386. https://doi.org/10.1007/978-3-319-50496-4_31 ↗
  67. Haoran Li, Jiajun Zhang, and Chengqing Zong. 2015. Predicting Implicit Discourse Relations with Purely Distributed Representations. In Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data (Guangzhou, China) (Lecture Notes in Computer Science). Springer, Berlin, Germany, 293–305. https://doi.org/10.1007/978-3-319-25816-4_24 ↗
  68. Haoran Li, Jiajun Zhang, and Chengqing Zong. 2017. Implicit discourse relation recognition for English and Chinese with multiview modeling and effective representation learning. ACM Trans. Asian Low-Resour. Lang. Inf. Process. 16, 3 (March 2017), 1–21. https://doi.org/10.1145/3028772 ↗
  69. Jiwei Li, Rumeng Li, and Eduard Hovy. 2014. Recursive deep models for discourse parsing. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (Doha, Qatar) (EMNLP ’14). The Association for Computational Linguistics, Stroudsburg, PA, USA, 2061–2069. https://doi.org/10.3115/v1/d14-1220 ↗
  70. Junyi Jessy Li, Marine Carpuat, and Ani Nenkova. 2014. Assessing the discourse factors that influence the quality of machine translation. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) (Baltimore, MD, USA) (ACL ’14). The Association for Computational Linguistics, Stroudsburg, PA, USA, 283–288. https://doi.org/10.3115/v1/p14-2047 ↗
  71. Junyi Jessy Li, Marine Carpuat, and Ani Nenkova. 2014. Cross-lingual Discourse Relation Analysis: A corpus study and a semi-supervised classification system. In Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers (Dublin, Ireland) (COLING ’14). The Association for Computer Linguistics, Stroudsburg, PA, USA, 577–587. https://www.aclweb.org/anthology/C14-1055/ ↗
  72. Junyi Jessy Li and Ani Nenkova. 2014. Reducing sparsity improves the recognition of implicit discourse relations. In Proceedings of the 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue (Philadelphia, PA, USA) (SIGDIAL ’14). The Association for Computer Linguistics, Stroudsburg, PA, USA, 199–207. https://doi.org/10.3115/v1/w14-4327 ↗
  73. Junyi Jessy Li and Ani Nenkova. 2016. The Instantiation discourse relation: A corpus analysis of its properties and improved detection. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (San Diego California, USA) (NAACL ’16). The Association for Computational Linguistics, Stroudsburg, PA, USA, 1181–1186. https://doi.org/10.18653/v1/n16-1141 ↗
  74. Sheng Li, Fang Kong, and Guodong Zhou. 2016. Recognizing PDTB Style Implicit Discourse Relations. Journal of Chinese Information Processing 30, 4 (2016), 81–89. http://jcip.cipsc.org.cn/CN/abstract/article_2250.shtml ↗
  75. Xiao Li, Yu Hong, Huibin Ruan, and Zhen Huang. 2020. Using a Penalty-based Loss Re-estimation Method to Improve Implicit Discourse Relation Classification. In Proceedings of the 28th International Conference on Computational Linguistics (Online) (COLING ’20). International Committee on Computational Linguistics, Barcelona, Spain, 1513– 1518. https://doi.org/10.18653/v1/2020.coling-main.132 ↗
  76. Zhongyi Li, Hai Zhao, Chenxi Pang, Lili Wang, and Huan Wang. 2016. A constituent syntactic parse tree based discourse parser. In Proceedings of the Twentieth Conference on Computational Natural Language Learning-Shared Task (Berlin, Germany) (CoNLL ’16). The Association for Computational Linguistics, Stroudsburg, PA, USA, 60–64. https://doi.org/10.18653/v1/k16-2008 ↗
  77. Maria Liakata, Simon Dobnik, Shyamasree Saha, Colin Batchelor, and Dietrich Rebholz Schuhmann. 2013. A discourse-driven content model for summarising scientific articles evaluated in a complex question answering task. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing (Seattle, Washington, USA) (EMNLP ’13). The Association for Computational Linguistics, Stroudsburg, PA, USA, 747–757. https://www.aclweb.org/anthology/D13-1070/ ↗
  78. Li Liang, Zheng Zhao, and Bonnie Webber. 2020. Extending Implicit Discourse Relation Recognition to the PDTB-3. In Proceedings of the First Workshop on Computational Approaches to Discourse (Online) (ACL ’20). The Association for Computational Linguistics, Stroudsburg, PA, USA, 135–147. https://doi.org/10.18653/v1/2020.codi-1.14 ↗
  79. Yankai Lin, Zhiyuan Liu, Maosong Sun, Yang Liu, and Xuan Zhu. 2015. Learning entity and relation embeddings for knowledge graph completion. In Proceedings of the AAAI Conference on Artificial Intelligence (Austin, Texas, USA) (AAAI ’15, Vol. 29). AAAI Press, Palo Alto, California, USA, 2181–2187. http://www.aaai.org/ocs/index.php/AAAI/AAAI15/paper/view/9571 ↗
  80. Ziheng Lin, Min-Yen Kan, and Hwee Tou Ng. 2009. Recognizing Implicit Discourse Relations in the Penn Discourse Treebank. In Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing (Singapore) (EMNLP ’09). The Association for Computer Linguistics, Stroudsburg, PA, USA, 343–351. https://www.aclweb.org/anthology/D09-1036/ ↗
  81. Wang Ling, Chris Dyer, Alan W Black, Isabel Trancoso, Ramón Fermandez, Silvio Amir, Luis Marujo, and Tiago Luís. 2015. Finding Function in Form: Compositional Character Models for Open Vocabulary Word Representation. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (Lisbon, Portugal) (EMNLP ’15). The Association for Computational Linguistics, Stroudsburg, PA, USA, 1520–1530. https://doi.org/10.18653/v1/d15-1176 ↗
  82. Xin Liu, Jiefu Ou, Yangqiu Song, and Xin Jiang. 2020. On the Importance of Word and Sentence Representation Learning in Implicit Discourse Relation Classification. In Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence (Yokohama, Japan) (IJCAI ’20). Elsevier, Amsterdam, Netherlands, 3830–3836. https://doi.org/10.24963/ijcai.2020/530 ↗
  83. Yang Liu and Sujian Li. 2016. Recognizing Implicit Discourse Relations via Repeated Reading: Neural Networks with Multi-Level Attention. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (Austin, Texas, USA) (EMNLP ’16). The Association for Computational Linguistics, Stroudsburg, PA, USA, 1224–1233. https://doi.org/10.18653/v1/d16-1130 ↗
  84. Yang Liu, Sujian Li, Xiaodong Zhang, and Zhifang Sui. 2016. Implicit discourse relation classification via multi-task neural networks. In Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (Phoenix, Arizona, USA) (AAAI ’16). AAAI Press, Palo Alto, California, USA, 2750–2756. http://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/view/11831 ↗
  85. Yang Liu, Jiajun Zhang, and Chengqing Zong. 2017. Memory Augmented Attention Model for Chinese Implicit Discourse Relation Recognition. In Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data (Nanjing, China) (Lecture Notes in Computer Science). Springer, Berlin, Germany, 411–423. https://doi.org/10.1007/978-3-319-69005-6_34 ↗
  86. Annie Louis, Aravind Joshi, Rashmi Prasad, and Ani Nenkova. 2010. Using entity features to classify implicit discourse relations. In Proceedings of the SIGDIAL 2010 Conference (Tokyo, Japan) (SIGDIAL ’10). The Association for Computer Linguistics, Stroudsburg, PA, USA, 59–62. https://www.aclweb.org/anthology/W10-4310/ ↗
  87. Annie Louis, Aravind K Joshi, and Ani Nenkova. 2010. Discourse indicators for content selection in Summarization. In Proceedings of the 11th Annual Meeting of the Special Interest Group on Discourse and Dialogue (Tokyo, Japan) (SIGDIAL ’10). The Association for Computer Linguistics, Stroudsburg, PA, USA, 147–156. https://www.aclweb.org/anthology/W10-4327/ ↗
  88. Christopher D. Manning. 2015. Computational Linguistics and Deep Learning. Comput. Linguist. 41, 4 (Dec. 2015), 701–707. https://doi.org/10.1162/COLI_a_00239 ↗
  89. Daniel Marcu and Abdessamad Echihabi. 2002. An unsupervised approach to recognizing discourse relations. In Proceedings of the 40th annual meeting of the association for computational linguistics (Philadelphia, PA, USA) (ACL ’02). The Association for Computer Linguistics, Stroudsburg, PA, USA, 368–375. https://doi.org/10.3115/1073083.1073145 ↗
  90. Mitchell P. Marcus, Grace Kim, Mary Ann Marcinkiewicz, Robert MacIntyre, Ann Bies, Mark Ferguson, Karen Katz, and Britta Schasberger. 1994. The Penn Treebank: Annotating Predicate Argument Structure. In Proceedings of the ARPA Human Language Technology Workshop (Plainsboro, New Jerey, USA). Morgan Kaufmann, San Francisco, CA, USA, 114–119. https://www.aclweb.org/anthology/H94-1020 ↗
  91. Mitchell P. Marcus, Mary Ann Marcinkiewicz, and Beatrice Santorini. 1993. Building a Large Annotated Corpus of English: The Penn Treebank. Comput. Linguist. 19, 2 (June 1993), 313–330. ↗
  92. Thomas Meyer and Andrei Popescu-Belis. 2012. Using Sense-labeled Discourse Connectives for Statistical Machine Translation. In Proceedings of the Joint Workshop on Exploiting Synergies between Information Retrieval and Machine Translation (ESIRMT) and Hybrid Approaches to Machine Translation (HyTra) (Avignon, France) (EACL ’12). The Association for Computational Linguistics, Stroudsburg, PA, USA, 129–138. https://www.aclweb.org/anthology/W12-0117/ ↗
  93. Thomas Meyer and Bonnie Webber. 2013. Implicitation of discourse connectives in (machine) translation. In Proceedings of the Workshop on Discourse in Machine Translation (Sofia, Bulgaria) (ACL ’13). The Association for Computational Linguistics, Stroudsburg, PA, USA, 19–26. https://www.aclweb.org/anthology/W13-3303/ ↗
  94. Todor Mihaylov and Anette Frank. 2016. Discourse relation sense classification using cross-argument semantic similarity based on word embeddings. In Proceedings of the Twentieth Conference on Computational Natural Language Learning-Shared Task (Berlin, Germany) (CoNLL ’16). The Association for Computational Linguistics, Stroudsburg, PA, USA, 100–107. https://doi.org/10.18653/v1/k16-2014 ↗
  95. Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Efficient estimation of word representations in vector space. (2013). arXiv:arXiv:1301.3781 ↗
  96. Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013. Distributed representations of words and phrases and their compositionality. In Advances in neural information processing systems (Lake Tahoe, Nevada, USA) (NeurIPS ’13). MIT Press, Cambridge, MA, USA, 3111–3119. http://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality ↗
  97. Eleni Miltsakaki, Rashmi Prasad, Aravind K. Joshi, and Bonnie L. Webber. 2004. The Penn Discourse TreeBank. In Proceedings of the Fourth International Conference on Language Resources and Evaluation (Lisbon, Portugal) (LREC ’04). European Language Resources Association, Paris, France, 1–4. http://www.lrec-conf.org/proceedings/lrec2004/summaries/618.htm ↗
  98. Kashif Munir, Hai Zhao, and Zuchao Li. 2021. Learning Context-Aware Convolutional Filters for Implicit Discourse Relation Classification. IEEE ACM Transactions on Audio, Speech, and Language Processing 29 (2021), 2421–2433. ↗
  99. Linh The Nguyen, Linh Van Ngo, Khoat Than, and Thien Huu Nguyen. 2019. Employing the Correspondence of Relations and Connectives to Identify Implicit Discourse Relations via Label Embeddings. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Florence, Italy) (ACL ’19). The Association for Computational Linguistics, Stroudsburg, PA, USA, 4201–4207. https://doi.org/10.18653/v1/p19-1411 ↗
  100. Thien Huu Nguyen and Ralph Grishman. 2015. Relation extraction: Perspective from convolutional neural networks. In Proceedings of the 1st Workshop on Vector Space Modeling for Natural Language Processing (Denver, Colorado, USA) (NAACL ’15). The Association for Computational Linguistics, Stroudsburg, PA, USA, 39–48. https://doi.org/10.3115/v1/w15-1506 ↗
  101. Allen Nie, Erin Bennett, and Noah Goodman. 2019. DisSent: Learning sentence representations from explicit discourse relations. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Florence, Italy) (ACL ’19). The Association for Computational Linguistics, Stroudsburg, PA, USA, 4497–4510. https://doi.org/10.18653/v1/p19-1442 ↗
  102. Noriki Nishida and Hideki Nakayama. 2018. Coherence Modeling Improves Implicit Discourse Relation Recognition. In Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue (Melbourne, Australia) (SIGDIAL ’18). The Association for Computational Linguistics, Stroudsburg, PA, USA, 344–349. https://doi.org/10.18653/v1/w18-5040 ↗
  103. Atsushi Otsuka, Toru Hirano, Chiaki Miyazaki, Ryo Masumura, Ryuichiro Higashinaka, Toshiro Makino, and Yoshihiro Matsuo. 2015. Discourse relation recognition by comparing various units of sentence expression with recursive neural network. In Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation (Shanghai, China) (PACLIC ’15). The Association for Computational Linguistics, Stroudsburg, PA, USA, 63–72. https://www.aclweb.org/anthology/Y15-1008/ ↗
  104. Martha Palmer, Daniel Gildea, and Paul Kingsbury. 2005. The proposition bank: An annotated corpus of semantic roles. Computational linguistics 31, 1 (2005), 71–106. https://doi.org/10.1162/0891201053630264 ↗
  105. Joonsuk Park and Claire Cardie. 2012. Improving implicit discourse relation recognition through feature set optimization. In Proceedings of the 13th Annual Meeting of the Special Interest Group on Discourse and Dialogue (Seoul, South Korea) (SIGDIAL ’12). The Association for Computer Linguistics, Stroudsburg, PA, USA, 108–112. https://www.aclweb.org/anthology/W12-1614/ ↗
  106. Emily Pitler, Annie Louis, and Ani Nenkova. 2009. Automatic sense prediction for implicit discourse relations in text. In Proceedings of the 47th Annual Meeting of the Association for Computational Linguistics and the 4th International Joint Conference on Natural Language Processing of the AFNLP (Singapore) (ACL ’09). The Association for Computer Linguistics, Stroudsburg, PA, USA, 683–691. https://www.aclweb.org/anthology/P09-1077/ ↗
  107. Emily Pitler, Mridhula Raghupathy, Hena Mehta, Ani Nenkova, Alan Lee, and Aravind K. Joshi. 2008. Easily Identifiable Discourse Relations. In 22nd International Conference on Computational Linguistics, Posters Proceedings (Manchester UK) (COLING ’08). The Association for Computer Linguistics, Stroudsburg, PA, USA, 87–90. https://www.aclweb.org/anthology/C08-2022/ ↗
  108. Rashmi Prasad, Nikhil Dinesh, Alan Lee, Eleni Miltsakaki, Livio Robaldo, Aravind K. Joshi, and Bonnie L. Webber. 2008. The Penn Discourse TreeBank 2.0. In Proceedings of the International Conference on Language Resources and Evaluation (Marrakech, Morocco) (LREC ’08). European Language Resources Association, Paris, France, 1–8. http://www.lrec-conf.org/proceedings/lrec2008/summaries/754.html ↗
  109. Rashmi Prasad, Eleni Miltsakaki, Nikhil Dinesh, Alan Lee, Aravind Joshi, Livio Robaldo, and Bonnie Webber. 2007. The Penn Discourse Treebank 2.0 Annotation Manual. Technical Report. University of Pennsylvania, Philadelphia, PA, USA. find
  110. Lianhui Qin, Zhisong Zhang, and Hai Zhao. 2016. Implicit discourse relation recognition with context-aware character-enhanced embeddings. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers (Osaka, Japan) (COLING ’16). The Association for Computational Linguistics, Stroudsburg, PA, USA, 1914–1924. https://www.aclweb.org/anthology/C16-1180/ ↗
  111. Lianhui Qin, Zhisong Zhang, and Hai Zhao. 2016. A stacking gated neural architecture for implicit discourse relation classification. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (Austin, Texas, USA) (EMNLP ’16). The Association for Computational Linguistics, Stroudsburg, PA, USA, 2263–2270. https://doi.org/10.18653/v1/d16-1246 ↗
  112. Lianhui Qin, Zhisong Zhang, Hai Zhao, Zhiting Hu, and Eric Xing. 2017. Adversarial Connective-exploiting Networks for Implicit Discourse Relation Classification. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (Vancouver, Canada) (ACL ’17). The Association for Computational Linguistics, Stroudsburg, PA, USA, 1006–1017. https://doi.org/10.18653/v1/P17-1093 ↗
  113. Samuel Rönnqvist, Niko Schenk, and Christian Chiarcos. 2017. A Recurrent Neural Model with Attention for the Recognition of Chinese Implicit Discourse Relations. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) (Vancouver, Canada) (ACL ’17). The Association for Computational Linguistics, Stroudsburg, PA, USA, 256–262. https://doi.org/10.18653/v1/P17-2040 ↗
  114. Michael Roth. 2017. Role Semantics for Better Models of Implicit Discourse Relations. In 12th International Conference on Computational Semantics—Short papers (Montpellier, France) (IWCS ’17). The Association for Computer Linguistics, Stroudsburg, PA, USA, 1–8. https://www.aclweb.org/anthology/W17-6934/ ↗
  115. Huibin Ruan, Yu Hong, Yang Xu, Zhen Huang, Guodong Zhou, and Min Zhang. 2020. Interactively-Propagative Attention Learning for Implicit Discourse Relation Recognition. In Proceedings of the 28th International Conference on Computational Linguistics (Online) (COLING ’20). International Committee on Computational Linguistics, Barcelona, Spain, 3168–3178. https://doi.org/10.18653/v1/2020.coling-main.282 ↗
  116. Attapol Rutherford, Vera Demberg, and Nianwen Xue. 2017. A systematic study of neural discourse models for implicit discourse relation. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers (Valencia, Spain) (EACL ’17). The Association for Computational Linguistics, Stroudsburg, PA, USA, 281–291. https://doi.org/10.18653/v1/e17-1027 ↗
  117. Attapol Rutherford and Nianwen Xue. 2014. Discovering implicit discourse relations through brown cluster pair representation and coreference patterns. In Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics (Gothenburg, Sweden) (EACL ’14). The Association for Computer Linguistics, Stroudsburg, PA, USA, 645–654. https://doi.org/10.3115/v1/e14-1068 ↗
  118. Attapol Rutherford and Nianwen Xue. 2015. Improving the inference of implicit discourse relations via classifying explicit discourse connectives. In Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Denver, Colorado, USA) (NAACL ’15). The Association for Computational Linguistics, Stroudsburg, PA, USA, 799–808. https://doi.org/10.3115/v1/n15-1081 ↗
  119. Niko Schenk, Christian Chiarcos, Kathrin Donandt, Samuel Rönnqvist, Evgeny Stepanov, and Giuseppe Riccardi. 2016. Do we really need all those rich linguistic features? a neural network-based approach to implicit sense labeling. In Proceedings of the Twentieth Conference on Computational Natural Language Learning-Shared Task (Berlin, Germany) (CoNLL ’16). The Association for Computational Linguistics, Stroudsburg, PA, USA, 41–49. https://doi.org/10.18653/v1/k16-2005 ↗
  120. Xiaohan She, Ping Jian, Pengcheng Zhang, and Heyan Huang. 2018. Leveraging hierarchical deep semantics to classify implicit discourse relations via a mutual learning method. ACM Trans. Asian Low-Resour. Lang. Inf. Process. 17, 3 (Feb. 2018), 1–12. https://doi.org/10.1145/3178456 ↗
  121. Yelong Shen, Xiaodong He, Jianfeng Gao, Li Deng, and Grégoire Mesnil. 2014. Learning semantic representations using convolutional neural networks for web search. In Proceedings of the 23rd International Conference on World Wide Web (Seoul, Republic of Korea) (WWW ’14). ACMPress, New York, NY, USA, 373–374. https://doi.org/10.1145/2567948.2577348 ↗
  122. Wei Shi and Vera Demberg. 2019. Learning to Explicitate Connectives with Seq2Seq Network for Implicit Discourse Relation Classification. In Proceedings of the 13th International Conference on Computational Semantics-Long Papers (Gothenburg, Sweden) (IWCS ’19). The Association for Computational Linguistics, Stroudsburg, PA, USA, 188–199. https://doi.org/10.18653/v1/w19-0416 ↗
  123. Wei Shi and Vera Demberg. 2019. Next sentence prediction helps implicit discourse relation classification within and across domains. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (Hong Kong, China) (EMNLP-IJCNLP ’19). The Association for Computational Linguistics, Stroudsburg, PA, USA, 5794–5800. https://doi.org/10.18653/v1/D19-1586 ↗
  124. Wei Shi, Frances Yung, Raphael Rubino, and Vera Demberg. 2017. Using explicit discourse connectives in translation for implicit discourse relation classification. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers) (Taipei, Taiwan) (IJCNLP ’17). The Association for Computational Linguistics, Stroudsburg, PA, USA, 484–495. https://www.aclweb.org/anthology/I17-1049/ ↗
  125. Swapna Somasundaran, Galileo Namata, Janyce Wiebe, and Lise Getoor. 2009. Supervised and unsupervised methods in employing discourse relations for improving opinion polarity classification. In Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing (Singapore) (EMNLP ’09). The Association for Computer Linguistics, Stroudsburg, PA, USA, 170–179. https://www.aclweb.org/anthology/D09-1018/ ↗
  126. Caroline Sporleder and Alex Lascarides. 2008. Using automatically labelled examples to classify rhetorical relations: An assessment. Natural Language Engineering 14, 3 (2008), 369–416. https://doi.org/10.1017/S1351324906004451 ↗
  127. Philip J Stone, Dexter C Dunphy, and Marshall S Smith. 1966. The general inquirer: A computer approach to content analysis. MIT press, Cambridge, MA, USA. find
  128. Kun Sun and Lili Zhang. 2018. Quantitative aspects of PDTB-style discourse relations across languages. Journal of Quantitative Linguistics 25, 4 (Jan. 2018), 342–371. https://doi.org/10.1080/09296174.2017.1390934 ↗
  129. Yu Sun, Huibin Ruan, Yu Hong, Chenghao Wu, Min Zhang, and Guodong Zhou. 2019. Multi-grain Representation Learning for Implicit Discourse Relation Recognition. In CCF International Conference on Natural Language Processing and Chinese Computing (Dunhuang, China) (Lecture Notes in Computer Science, Vol. 11838). Springer, Berlin, Germany, 725–736. https://doi.org/10.1007/978-3-030-32233-5_56 ↗
  130. Ilya Sutskever, Joshua B Tenenbaum, and Russ R Salakhutdinov. 2009. Modelling relational data using bayesian clustered tensor factorization. In Advances in neural information processing systems (Vancouver, British Columbia, Canada) (NeurIPS ’09). MIT Press, Cambridge, MA, USA, 1821–1828. http://papers.nips.cc/paper/3863-modelling-relational-data-using-bayesian-clustered-tensor-factorization ↗
  131. Wen tau Yih, Xiaodong He, and Christopher Meek. 2014. Semantic parsing for single-relation question answering. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Baltimore, MD, USA) (ACL ’14). The Association for Computational Linguistics, Stroudsburg, PA, USA, 643–648. https://doi.org/10.3115/v1/p14-2105 ↗
  132. Siddharth Varia, Christopher Hidey, and Tuhin Chakrabarty. 2019. Discourse Relation Prediction: Revisiting Word Pairs with Convolutional Networks. In Proceedings of the 20th Annual SIGdial Meeting on Discourse and Dialogue (Stockholm, Sweden) (SIGDIAL ’19). The Association for Computational Linguistics, Stroudsburg, PA, USA, 442–452. https://doi.org/10.18653/v1/W19-5951 ↗
  133. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in neural information processing systems (Long Beach, CA, USA) (NeurIPS ’17). MIT Press, Cambridge, MA, USA, 5998–6008. http://papers.nips.cc/paper/7181-attention-is-all-you-need ↗
  134. Suzan Verberne, Lou Boves, Nelleke Oostdijk, and Peter-Arno Coppen. 2007. Evaluating discourse-based answer extraction for why-question answering. In Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval (Amsterdam, The Netherlands) (SIGIR ’07). ACMPress, New York, NY, USA, 735–736. https://doi.org/10.1145/1277741.1277883 ↗
  135. Chang Wang and Bang Wang. 2020. Encoding Sentences with a Syntax-Aware Self-attention Neural Network for Emotion Distribution Prediction. In CCF International Conference on Natural Language Processing and Chinese Computing (Zhengzhou, China) (Lecture Notes in Computer Science). Springer, Berlin, Germany, 256–266. https://doi.org/10.1007/978-3-030-60457-8_21 ↗
  136. Chang Wang and Bang Wang. 2020. An End-to-end Topic-Enhanced Self-Attention Network for Social Emotion Classification. In Proceedings of The Web Conference 2020 (Taipei, Taiwan) (WWW ’20). ACMPress, New York, NY, USA, 2210–2219. https://doi.org/10.1145/3366423.3380286 ↗
  137. Chang Wang, Bang Wang, Wei Xiang, and Minghua Xu. 2019. Encoding Syntactic Dependency and Topical Information for Social Emotion Classification. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (Paris, France) (SIGIR ’19). ACMPress, New York, NY, USA, 881–884. https://doi.org/10.1145/3331184.3331287 ↗
  138. Chang Wang, Bang Wang, and Minghua Xu. 2019. Tree-Structured Neural Networks With Topic Attention for Social Emotion Classification. IEEE Access 7 (2019), 95505–95515. https://doi.org/10.1109/ACCESS.2019.2929204 ↗
  139. Jianxiang Wang and Man Lan. 2016. Two end-to-end shallow discourse parsers for English and Chinese in CoNLL-2016 shared task. In Proceedings of the Twentieth Conference on Computational Natural Language Learning-Shared Task (Berlin, Germany) (CoNLL ’16). The Association for Computational Linguistics, Stroudsburg, PA, USA, 33–40. https://doi.org/10.18653/v1/k16-2004 ↗
  140. Wenting Wang, Jian Su, and Chew Lim Tan. 2010. Kernel Based Discourse Relation Recognition with Temporal Ordering Information. In Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics (Uppsala, Sweden) (ACL ’10). The Association for Computer Linguistics, Stroudsburg, PA, USA, 710–719. https://www.aclweb.org/anthology/P10-1073/ ↗
  141. Xun Wang, Sujian Li, Jiwei Li, and Wenjie Li. 2012. Implicit discourse relation recognition by selecting typical training examples. In Proceedings of COLING 2012, the 23th International Conference on Computational Linguistics: Technical Papers (Mumbai, India) (COLING ’12). The Association for Computational Linguistics, Stroudsburg, PA, USA, 2757–2772. https://www.aclweb.org/anthology/C12-1168/ ↗
  142. Yizhong Wang, Sujian Li, Jingfeng Yang, Xu Sun, and Houfeng Wang. 2017. Tag-Enhanced Tree-Structured Neural Networks for Implicit Discourse Relation Classification. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers) (Taipei, Taiwan) (IJCNLP ’17). The Association for Computational Linguistics, Stroudsburg, PA, USA, 496–505. https://www.aclweb.org/anthology/I17-1050/ ↗
  143. Zhen Wang, Jianwen Zhang, Jianlin Feng, and Zheng Chen. 2014. Knowledge graph embedding by translating on hyperplanes. In Proceedings of the AAAI Conference on Artificial Intelligence (Quebec city, Quebec, Canada) (AAAI ’14, Vol. 28). AAAI Press, Palo Alto, California, USA, 1112–1119. http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8531 ↗
  144. Bonnie Webber, Rashmi Prasad, Alan Lee, and Aravind Joshi. 2019. The Penn Discourse Treebank 3.0 Annotation Manual. Technical Report. University of Pennsylvania, Philadelphia, PA, USA. find
  145. Gregor Weiss and Marko Bajec. 2016. Discourse sense classification from scratch using focused rnns. In Proceedings of the Twentieth Conference on Computational Natural Language Learning-Shared Task (Berlin, Germany) (CoNLL ’16). The Association for Computational Linguistics, Stroudsburg, PA, USA, 50–54. https://doi.org/10.18653/v1/k16-2006 ↗
  146. Theresa Wilson, Janyce Wiebe, and Paul Hoffmann. 2005. Recognizing contextual polarity in phrase-level sentiment analysis. In Proceedings of human language technology conference and conference on empirical methods in natural language processing (Vancouver, British Columbia, Canada) (EMNLP ’05). The Association for Computational Linguistics, Stroudsburg, PA, USA, 347–354. https://www.aclweb.org/anthology/H05-1044/ ↗
  147. Changxing Wu, Chaowen Hu, Ruochen Li, Hongyu Lin, and Jinsong Su. 2020. Hierarchical multi-task learning with CRF for implicit discourse relation recognition. Knowl. Based Syst. 195 (2020), 105637. https://doi.org/10.1016/j. knosys.2020.105637 ↗
  148. Changxing Wu, Xiaodong Shi, Yidong Chen, Yanzhou Huang, and Jinsong Su. 2016. Bilingually-constrained synthetic data for implicit discourse relation recognition. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (Austin, Texas, USA) (EMNLP ’16). The Association for Computational Linguistics, Stroudsburg, PA, USA, 2306–2312. https://doi.org/10.18653/v1/d16-1253 ↗
  149. Changxing Wu, Xiaodong Shi, Yidong Chen, Yanzhou Huang, and Jinsong Su. 2017. Leveraging bilingually-constrained synthetic data via multi-task neural networks for implicit discourse relation recognition. Neurocomputing 243 (2017), 69–79. https://doi.org/10.1016/j.neucom.2017.02.084 ↗
  150. Changxing Wu, Xiaodong Shi, Yidong Chen, Jinsong Su, and Boli Wang. 2017. Improving implicit discourse relation recognition with discourse-specific word embeddings. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) (Vancouver, Canada) (ACL ’17). The Association for Computational Linguistics, Stroudsburg, PA, USA, 269–274. https://doi.org/10.18653/v1/P17-2042 ↗
  151. Changxing Wu, Xiaodong Shi, Jinsong Su, Yidong Chen, and Yanzhou Huang. 2017. Co-training for implicit discourse relation recognition based on manual and distributed features. Neural Processing Letters 46, 1 (2017), 233–250. https://doi.org/10.1007/s11063-017-9582-x ↗
  152. Changxing Wu, Jinsong Su, Yidong Chen, and Xiaodong Shi. 2019. Boosting implicit discourse relation recognition with connective-based word embeddings. Neurocomputing 369 (2019), 39–49. https://doi.org/10.1016/j.neucom.2019.08.081 ↗
  153. Wei Xiang and Bang Wang. 2019. A Survey of Event Extraction From Text. IEEE Access 7 (Nov. 2019), 173111–173137. https://doi.org/10.1109/ACCESS.2019.2956831 ↗
  154. Han Xiao, Minlie Huang, and Xiaoyan Zhu. 2016. TransG: A Generative Model for Knowledge Graph Embedding. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (Berlin, Germany) (ACL ’16). The Association for Computational Linguistics, Stroudsburg, PA, USA, 2316–2325. https://doi.org/10.18653/v1/p16-1219 ↗
  155. Sheng Xu, Peifeng Li, Fang Kong, Qiaoming Zhu, and Guodong Zhou. 2019. Topic Tensor Network for Implicit Discourse Relation Recognition in Chinese. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (Florence, Italy) (ACL ’19). The Association for Computational Linguistics, Stroudsburg, PA, USA, 608–618. https://doi.org/10.18653/v1/p19-1058 ↗
  156. Yang Xu, Yu Hong, Huibin Ruan, Jianmin Yao, Min Zhang, and Guodong Zhou. 2018. Using active learning to expand training data for implicit discourse relation recognition. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (Brussels, Belgium) (EMNLP ’18). The Association for Computational Linguistics, Stroudsburg, PA, USA, 725–731. https://doi.org/10.18653/v1/d18-1079 ↗
  157. Yu Xu, Man Lan, Yue Lu, Zheng Yu Niu, and Chew Lim Tan. 2012. Connective prediction using machine learning for implicit discourse relation classification. In The 2012 International Joint Conference on Neural Networks (Brisbane, Australia) (IJCNN ’12). IEEE Press, New York, NY, USA, 1–8. https://doi.org/10.1109/IJCNN.2012.6252548 ↗
  158. Nianwen Xue, Hwee Tou Ng, Sameer Pradhan, Rashmi Prasad, Christopher Bryant, and Attapol Rutherford. 2015. The conll-2015 shared task on shallow discourse parsing. In Proceedings of the Nineteenth Conference on Computational Natural Language Learning-Shared Task (Beijing, China) (CoNLL ’15). The Association for Computational Linguistics, Stroudsburg, PA, USA, 1–16. https://doi.org/10.18653/v1/k15-2001 ↗
  159. Nianwen Xue, Hwee Tou Ng, Sameer Pradhan, Attapol Rutherford, Bonnie Webber, Chuan Wang, and Hongmin Wang. 2016. Conll 2016 shared task on multilingual shallow discourse parsing. In Proceedings of the Twentieth Conference on Computational Natural Language Learning-Shared Task (Berlin, Germany) (CoNLL ’16). The Association for Computational Linguistics, Stroudsburg, PA, USA, 1–19. https://doi.org/10.18653/v1/k16-2001 ↗
  160. Weirong Yan, Yang Xu, Shanshan Zhu, Yu Hong, Jianmin Yao, and Qiaoming Zhu. 2016. A Survey to Discourse Relation Analyzing. Journal of Chinese Information Processing 30, 4 (2016), 1–11. http://jcip.cipsc.org.cn/CN/abstract/article_2241.shtml ↗
  161. Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Russ R Salakhutdinov, and Quoc V Le. 2019. Xlnet: Generalized autoregressive pretraining for language understanding. In Advances in neural information processing systems (Vancouver, BC, Canada) (NeurIPS ’19). MIT Press, Cambridge, MA, USA, 5753–5763. http://papers.nips.cc/paper/8812-xlnet-generalized-autoregressive-pretraining-for-language-understanding ↗
  162. Yasuhisa Yoshida, Jun Suzuki, Tsutomu Hirao, and Masaaki Nagata. 2014. Dependency-based discourse parser for single-document summarization. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (Doha, Qatar) (EMNLP ’14). The Association for Computer Linguistics, Stroudsburg, PA, USA, 1834–1839. https://doi.org/10.3115/v1/d14-1196 ↗
  163. Xihan Yue, Luoyi Fu, and Xinbing Wang. 2018. Externally controllable RNN for implicit discourse relation classification. In National CCF Conference on Natural Language Processing and Chinese Computing (Dalian, China) (Lecture Notes in Computer Science). Springer, Berlin, Germany, 158–169. https://doi.org/10.1007/978-3-319-73618-1_14 ↗
  164. Daojian Zeng, Kang Liu, Siwei Lai, Guangyou Zhou, Jun Zhao, et al. 2014. Relation classification via convolutional deep neural network. In 25th International Conference on Computational Linguistics, Proceedings of the Conference: Technical Papers (Dublin, Ireland) (COLING ’14). The Association for Computational Linguistics, Stroudsburg, PA, USA, 2335–2344. https://www.aclweb.org/anthology/C14-1220/ ↗
  165. Biao Zhang, Jinsong Su, Deyi Xiong, Yaojie Lu, Hong Duan, and Junfeng Yao. 2015. Shallow convolutional neural network for implicit discourse relation recognition. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (Lisbon, Portugal) (EMNLP ’15). The Association for Computational Linguistics, Stroudsburg, PA, USA, 2230–2235. https://doi.org/10.18653/v1/d15-1266 ↗
  166. Biao Zhang, Deyi Xiong, Jinsong Su, Qun Liu, Rongrong Ji, Hong Duan, and Min Zhang. 2016. Variational Neural Discourse Relation Recognizer. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing (Austin, Texas, USA) (EMNLP ’16). The Association for Computational Linguistics, Stroudsburg, PA, USA, 382–391. https://doi.org/10.18653/v1/d16-1037 ↗
  167. Biao Zhang, Deyi Xiong, Jinsong Su, and Min Zhang. 2018. Learning better discourse representation for implicit discourse relation recognition via attention networks. Neurocomputing 275 (2018), 1241–1249. https://doi.org/10.1016/j.neucom.2017.09.074 ↗
  168. Yingxue Zhang, Fandong Meng, Peng Li, Ping Jian, and Jie Zhou. 2021. Context Tracking Network: Graph-based Context Modeling for Implicit Discourse Relation Recognition. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Online) (NAACL ’21). The Association for Computational Linguistics, Stroudsburg, PA, USA, 1592–1599. https://doi.org/10.18653/v1/2021.naacl-main.126 ↗
  169. Meilin Zhou, Qi Liang, Lu Ma, Dan Luo, Peng Zhang, and Bin Wang. 2020. Towards Selective Data Enhanced Implicit Discourse Relation Recognition via Reinforcement Learning. In 2020 International Joint Conference on Neural Networks (Glasgow, United Kingdom) (IJCNN ’20). IEEE, IEEE Press, New York, NY, USA, 1–8. https://doi.org/10.1109/IJCNN48605.2020.9207006 ↗
  170. Meilin Zhou, Qi Liang, Peng Zhang, Lu Ma, Dan Luo, and Bin Wang. 2020. Global Context-aware Representation for Implicit Discourse Relation Recognition. In 2020 IEEE Intl Conf on Parallel Distributed Processing with Applications, Big Data Cloud Computing, Sustainable Computing Communications, Social Computing Networking (ISPA/BDCloud/SocialCom/SustainCom) (Exeter, United Kingdom) (BdCloud ’20). IEEE Press, New York, NY, USA, 458–465. https://doi.org/10.1109/ISPA-BDCloud-SocialCom-SustainCom51426.2020.00083 ↗
  171. Peng Zhou, Wei Shi, Jun Tian, Zhenyu Qi, Bingchen Li, Hongwei Hao, and Bo Xu. 2016. Attention-based bidirectional long short-term memory networks for relation classification. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) (Berlin, Germany) (ACL ’16). The Association for Computational Linguistics, Stroudsburg, PA, USA, 207–212. https://doi.org/10.18653/v1/p16-2034 ↗
  172. Xiaopei Zhou, Yu Hong, Tingting Che, Jianmin Yao, and Qiaoming Zhu. 2013. An Unsupervised Approach to Inferring Implicit Discourse Relation. Journal of Chinese Information Processing 27, 2 (2013), 17–25. https://kns.cnki.net/kcms/detail/detail.aspx?FileName=MESS201302003&DbName=CJFQ2013 ↗
  173. Yuping Zhou and Nianwen Xue. 2012. PDTB-style discourse annotation of Chinese text. In Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (Jeju Island, Korea) (ACL ’12). The Association for Computational Linguistics, Stroudsburg, PA, USA, 69–77. https://www.aclweb.org/anthology/P12-1008/ ↗
  174. Zhi Min Zhou, Man Lan, Zheng-Yu Niu, Yu Xu, and Jian Su. 2010. The effects of discourse connectives prediction on implicit discourse relation recognition. In Proceedings of the SIGDIAL 2010 Conference (Tokyo, Japan) (SIGDIAL ’10). The Association for Computer Linguistics, Stroudsburg, PA, USA, 139–146. https://www.aclweb.org/anthology/W10-4326/ ↗
  175. Zhi-Min Zhou, Yu Xu, Zheng-Yu Niu, Man Lan, Jian Su, and Chew Lim Tan. 2010. Predicting discourse connectives for implicit discourse relation recognition. In Proceedings of the 23rd International Conference on Computational Linguistics: Posters (Beijing, China) (COLING ’10). Chinese Information Processing Society of China, Beijing, China, 1507–1514. https://www.aclweb.org/anthology/C10-2172/ ↗

Updated

↑ back to top

This page in Markdown → https://md.research.elamur.ai/free/en/papers/xiang-2022-survey-implicit-discourse-relation-recognition/