The CoNLL-2015 Shared Task on Shallow Discourse Parsing
Summary
The CoNLL-2015 shared task introduced Shallow Discourse Parsing as a community benchmark. Given English news text, a system must find individual discourse relations. This involves locating any explicit connective, marking the two argument spans as Arg1 and Arg2, and assigning a sense label. Relations may be explicit (signalled by words like "because" or "however") or non-explicit (Implicit, AltLex, EntRel, NoRel). The organizers adapted the Penn Discourse TreeBank (PDTB 2.0), built on the WSJ corpus, for training and development. For a blind test, they newly annotated Wikinews articles following PDTB guidelines. Sixteen teams took part. For the first time in CoNLL, systems were deployed on remote virtual machines and run through the TIRA web platform, so participants never saw the test data. Full end-to-end parsing proved very hard: the strongest system reached only 24.0% F1 on the blind set, largely because errors compound across pipeline stages. The released data and scorer provide a lasting benchmark for discourse parsing research.
Key points
- Data splits: PDTB 2.0 Sections 2–21 were used for training, Section 22 for development, and Section 23 as a standard test set. Official rankings relied on a separate blind test set of 30,158 words drawn from English Wikinews.
- PDTB 2.0 relation counts: the corpus holds 40,600 relations, broken down as follows:
- 18,459 Explicit
- 16,053 Implicit
- 624 AltLex
- 5,210 EntRel
- 254 NoRel
- Sense labels: senses follow a three-level hierarchy with 4 top classes and 16 second-level types. Attribution and supplementary-span annotations were left out of the task.
- Evaluation setup: 16 teams from three continents submitted systems through the TIRA platform. This kept the test data hidden and the evaluation reproducible.
- Results: the best end-to-end score on the blind test was 24.0% F1. Non-Explicit relations were much harder to handle than Explicit ones, and some teams tackled only the Explicit subtask.
Cited by
- Where Are We in Discourse Relation Recognition?
- The Change that Matters in Discourse Parsing: Estimating the Impact of Domain Shift on Parser Error
- Learning Connective-based Word Representations for Implicit Discourse Relation Identification
- Implicit Discourse Relation Detection via a Deep Architecture with Gated Relevance Network
- Improving Implicit Discourse Relation Classification by Modeling Inter-dependencies of Discourse Units in a Paragraph
- A Regularization Approach for Incorporating Event Knowledge and Coreference Relations into Neural Discourse Parsing
- Comparing PTB and UD information for PDTB discourse connective identification
- Implicit Discourse Relation Classification: We Need to Talk about Evaluation
- Adapting BERT to Implicit Discourse Relation Classification with a Focus on Discourse Connectives
- Contextualized Embeddings for Connective Disambiguation in Shallow Discourse Parsing
- discopy: A Neural System for Shallow Discourse Parsing
- Assessing Discourse Relations in Language Generation from GPT-2
- Extending Implicit Discourse Relation Recognition to the PDTB-3
- Discourse Relation Sense Classification Using Cross-argument Semantic Similarity Based on Word Embeddings
- OPT: Oslo–Potsdam–Teesside. Pipelining Rules, Rankers, and Classifier Ensembles for Shallow Discourse Parsing
- Adapting Event Embedding for Implicit Discourse Relation Recognition
- Cross-Sentence N-ary Relation Extraction with Graph LSTMs
- Towards Full Text Shallow Discourse Relation Annotation: Experiments with Cross-Paragraph Implicit Relations in the PDTB
- Discourse Annotation in the PDTB: The Next Generation
- QADiscourse - Discourse Relations as QA Pairs: Representation, Crowdsourcing and Baselines
- Implicit Discourse Relation Recognition with Context-aware Character-enhanced Embeddings
- Shallow Discourse Parsing Using Convolutional Neural Network
- A Stacking Gated Neural Architecture for Implicit Discourse Relation Classification
- Adversarial Connective-exploiting Networks for Implicit Discourse Relation Classification
- Discourse Coherence: Concurrent Explicit and Implicit Relations
- Robust Non-Explicit Neural Discourse Parser in English and Chinese
- A Systematic Study of Neural Discourse Models for Implicit Discourse Relation
- On the Need of Cross Validation for Discourse Relation Classification
- Acquiring Annotated Data with Cross-lingual Explicitation for Implicit Discourse Relation Classification
- Learning to Explicitate Connectives with Seq2Seq Network for Implicit Discourse Relation Classification
- Next Sentence Prediction helps Implicit Discourse Relation Classification within and across Domains
- The UniTN Discourse Parser in CoNLL 2015 Shared Task: Token-level Sequence Labeling with Argument-specific Models
- UniTN End-to-End Discourse Parser for CoNLL 2016 Shared Task
- A Refined End-to-End Discourse Parser
- Ambiguity in Explicit Discourse Connectives
- CoNLL 2016 Shared Task on Multilingual Shallow Discourse Parsing
- Revisiting Shallow Discourse Parsing in the PDTB-3: Handling Intra-sentential Implicits
Cite
@inproceedings{xue2015conll,
title = {{The CoNLL-2015 Shared Task on Shallow Discourse Parsing}},
author = {Xue, Nianwen and Ng, Hwee Tou and Pradhan, Sameer and Prasad, Rashmi and Bryant, Christopher and Rutherford, Attapol},
booktitle = {Proceedings of the Nineteenth Conference on Computational Natural Language Learning - Shared Task},
year = {2015},
doi = {10.18653/v1/k15-2001},
url = {https://doi.org/10.18653/v1/k15-2001}
}References
- 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. find our card
- Lynn Carlson, Daniel Marcu, and Mary Ellen Okurowski. 2003. Building a discourse-tagged corpus in the framework of Rhetorical Structure Theory. In Current Directions in Discourse and Dialogue. find our card
- 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. find
- Sobha Lalitha Devi, Sindhuja Gopalan, Lakshmi S, Pattabhi RK Rao, Vijay Sundar Ram R., and Malarkodi C.S. 2015. A hybrid discourse relation parser in CoNLL 2015. In Proceedings of the Nineteenth Conference on Computational Natural Language Learning: Shared Task. find
- David A Duverle and Helmut Prendinger. 2009. A novel discourse parser based on support vector machine classification. In Proceedings of the Joint Conference of the 47th Annual Meeting of the Association of Computational Lingustics and the 4th International Joint Conference on Natural Language Processing of the AFNLP. find our card
- Vanessa Wei Feng and Graeme Hirst. 2012. Text-level discourse parsing with rich linguistic features. In Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics. find our card
- Vanessa Wei Feng and Graeme Hirst. 2014. A linear-time bottom-up discourse parser with constraints and post-editing. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics. find our card
- Sucheta Ghosh, Giuseppe Riccardi, and Richard Johansson. 2012. Global features for shallow discourse parsing. In Proceedings of the 13th Annual Meeting of the Special Interest Group on Discourse and Dialogue. find our card
- Tim Gollub, Benno Stein, and Steven Burrows. 2012. Ousting Ivory Tower Research: Towards a Web Framework for Providing Experiments as a Service. In Bill Hersh, Jamie Callan, Yoelle Maarek, and Mark Sanderson, editors, 35th International ACM Conference on Research and Development in Information Retrieval (SIGIR 12), pages 1125–1126. ACM, August. find
- Yangfeng Ji and Jacob Eisenstein. 2014. Representation learning for text-level discourse parsing. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics. find our card
- Fang Kong, Sheng Li, and Guodong Zhou. 2015. The SoNLP-DP system in the CoNLL-2015 shared task. In Proceedings of the Nineteenth Conference on Computational Natural Language Learning: Shared Task. find
- Man Lan, Yu Xu, and Zhengyu 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. find our card
- Junyi Jessy Li and Ani Nenkova. 2014. Addressing class imbalance for improved recognition of implicit discourse relations. In Proceedings of the 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue. find
- Sujian Li, Liang Wang, Ziqiang Cao, and Wenjie Li. 2014. Text-level discourse dependency parsing. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics. find
- 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. find our card
- Ziheng Lin, Hwee Tou Ng, and Min-Yen Kan. 2014. A PDTB-styled end-to-end discourse parser. Natural Language Engineering, 20(2):151–184. find
- William C Mann and Sandra A Thompson. 1988. Rhetorical Structure Theory: Toward a functional theory of text organization. Text, 8(3):243–281. ↗ our card
- Christopher D. Manning, Mihai Surdeanu, John Bauer, Jenny Finkel, Steven J. Bethard, and David Mc-Closky. 2014. The Stanford CoreNLP natural language processing toolkit. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics. find our card
- Mitchell P. Marcus, Beatrice Santorini, and Mary Ann Marcinkiewicz. 1993. Building a large annotated corpus of English: The Penn Treebank. Computational Linguistics, 19(2):313–330. ↗ our card
- Eleni Miltsakaki, Rashmi Prasad, Aravind Joshi, and Bonnie Webber. 2004. Annotating discourse connectives and their arguments. In Proceedings of the Human Language Technology/North American Chapter of the Association for Computational Linguistics Workshop on Frontiers in Corpus Annotation. find our card
- Truong Son Nguyen, Bao Quoc Ho, and Le Minh Nguyen. 2015. JAIST: A two-phase machine learning approach for identifying discourse relations in newswire texts. In Proceedings of the Nineteenth Conference on Computational Natural Language Learning: Shared Task. find
- Martha Palmer, Daniel Gildea, and Paul Kingsbury. 2005. The Proposition Bank: An annotated corpus of semantic roles. Computational Linguistics, 31(1):71–106. ↗ our card
- 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. find our card
- Slav Petrov and Dan Klein. 2007. Improved inferencing for unlexicalized parsing. In Proceedings of the Human Language Technology/North American Chapter of the Association for Computational Linguistics. find
- Emily Pitler, Mridhula Raghupathy, Hena Mehta, Ani Nenkova, Alan Lee, and Aravind K Joshi. 2008. Easily identifiable discourse relations. Technical report, University of Pennsylvania. find our card
- Emily Pitler, Annie Louis, and Ani Nenkova. 2009. Automatic sense prediction for implicit discourse relations in text. In Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP. find our card
- Martin Potthast, Tim Gollub, Francisco Rangel, Paolo Rosso, Efstathios Stamatatos, and Benno Stein. 2014. Improving the Reproducibility of PAN’s Shared Tasks: Plagiarism Detection, Author Identification, and Author Profiling. In Evangelos Kanoulas, Mihai Lupu, Paul Clough, Mark Sanderson, Mark Hall, Allan Hanbury, and Elaine Toms, editors, Information Access Evaluation meets Multilinguality, Multimodality, and Visualization. 5th International Conference of the CLEF Initiative (CLEF 14), pages 268–299, Berlin Heidelberg New York, September. Springer. find
- Sameer Pradhan, Lance Ramshaw, Mitchell Marcus, Martha Palmer, Ralph Weischedel, and Nianwen Xue. 2011. CoNLL-2011 shared task: Modeling unrestricted coreference in OntoNotes. In Proceedings of the Fifteenth Conference on Computation Natural Language Learining: Shared Task. find our card
- Sameer Pradhan, Alessandro Moschitti, Nianwen Xue, Olga Uryupina, and Yuchen Zhang. 2012. CoNLL-2012 Shared Task: Modeling multilingual unrestricted coreference in OntoNotes. In Proceedings of the Sixteenth Conference on Computation Natural Language Learining: Shared Task. find our card
- Rashmi Prasad and Harry Bunt. 2015. Semantic relations in discourse: The current state of ISO 24617-8. In Proceedings of the 11th Joint ACL-ISO Workshop on Interoperable Semantic Annotation. find
- Rashmi Prasad, Nikhil Dinesh, Alan Lee, Eleni Miltsakaki, Livio Robaldo, Aravind Joshi, and Bonnie Webber. 2008. The Penn Discourse Treebank 2.0. In Proceedings of the 6th International Conference on Language Resources and Evaluation. find our card
- Rashmi Prasad, Bonnie Webber, and Aravind Joshi. 2014. Reflections on the Penn Discourse Treebank, comparable corpora, and complementary annotation. Computational Linguistics, 40(4):921–950. ↗ our card
- 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. find our card
- Yangqiu Song, Haoruo Peng, Parisa Kordjamshidi, Mark Sammons, and Dan Roth. 2015. Improving a pipeline architecture for shallow discourse parsing. In Proceedings of the Nineteenth Conference on Computational Natural Language Learning: Shared Task. find
- Manfred Stede. 2012. Discourse Processing. Morgan & Claypool Publishers. find
- Evgeny Stepanov, Giuseppe Riccardi, and Ali Orkan Bayer. 2015. The UniTN discourse parser in CoNLL 2015 shared task: Token-level sequence labeling with argument-specific models. In Proceedings of the Nineteenth Conference on Computational Natural Language Learning: Shared Task. find our card
- Rajen Subba and Barbara Di Eugenio. 2009. An effective discourse parser that uses rich linguistic information. In Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics. find our card
- Jianxiang Wang and Man Lan. 2015. A refined end-to-end discourse parser. In Proceedings of the Nineteenth Conference on Computational Natural Language Learning: Shared Task. find our card
- Xun Wang, Sujian Li, Jiwei Li, and Wenjie Li. 2012. Implicit discourse relation recognition by selecting typical training examples. In Proceedings of the 24th International Conference on Computational Linguistics. find our card
- Longyue Wang, Chris Hokamp, Tsuyoshi Okita, Xiaojun Zhang, and Qun Liu. 2015. The DCU discourse parser for connective, argument identification and explicit sense classification. In Proceedings of the Nineteenth Conference on Computational Natural Language Learning: Shared Task. find our card
- Bonnie Webber, Markus Egg, and Valia Kordoni. 2012. Discourse structure and language technology. Natural Language Engineering, 18(4):437–490. find
- Yasuhisa Yoshida, Katsuhiko Hayashi, Tsutomu Hirao, and Masaaki Nagata. 2015. Hybrid approach to PDTB-styled discourse parsing for CoNLL-2015. In Proceedings of the Nineteenth Conference on Computational Natural Language Learning: Shared Task. find
- 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. find our card
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