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A Transformer-based approach to Irony and Sarcasm detection

Written for the Research library (claude-opus-5-5) from the paper text, in our own words: the license "arXiv — non-exclusive licence to distribute" does not allow redistributing the text itself. Original — arxiv.org/pdf/1911.10401

Summary

Figurative language on social media, such as irony, sarcasm and metaphor, often signals a sentiment that contradicts the literal wording. This confuses standard sentiment analysis systems. Extending their earlier DESC system, the authors build a hybrid classifier called RCNN-RoBERTa. It places a recurrent convolutional neural network (RCNN) on top of the pre-trained RoBERTa transformer. The pipeline uses no hand-crafted features or lexicon lookups, and lowercasing is its only preprocessing step. They test the model on four benchmark datasets covering irony detection, sarcasm detection and sentiment scoring of figurative tweets. Baselines include ELMo, USE, NBSVM, FastText, XLnet, BERT, plain RoBERTa and previously published shared-task systems. On the three binary classification tasks, the model achieves the best reported accuracy, F1 and AUC. The RCNN layer adds roughly 2–5 F1 points over RoBERTa alone. The study shows that transfer learning with almost no preprocessing can outperform feature-heavy pipelines on non-literal language. That makes such detectors cheaper to build and scale.

Key points

  • Datasets and results:
    • SemEval-2018 Task 3.A (ironic tweets): accuracy 0.82, F1 0.80 and AUC 0.89. The prior DESC system reached F1 0.73.
    • Reddit SARC2.0 politics: accuracy 0.79, F1 0.78 and AUC 0.85.
    • Riloff Twitter sarcasm dataset: accuracy 0.91, F1 0.90 and AUC 0.94.
  • On SemEval-2015 Task 11 (figurative sentiment scores from -5 to 5), the model lowers MSE to 1.45, compared with 2.48 for DESC, an improvement of about 33.5%. However, DESC keeps a slightly higher cosine similarity (0.82 vs. 0.81). The paper's conclusion claims superiority on all metrics, but this result contradicts that claim.
  • Adding the RCNN on top of RoBERTa helped especially on the imbalanced datasets (Riloff, SemEval-2015).
  • A one-way ANOVA with Tukey test found the gains over the best published results statistically significant: p = 0.015 for ironic tweets and p = 0.003 for Riloff sarcasm.
  • The authors describe this as the first use of an unsupervised pre-trained transformer for detecting multiple forms of figurative language.

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

Fields: Linguistics & languages

Cited by

34 papers of our library cite it (of the 140673 whose bibliographies we read); 2 of them have a page here, 32 not yet.

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.1007/s00521-020-05102-3 · arXiv 1911.10401

@misc{potamias2019transformer,
  title = {{A Transformer-based approach to Irony and Sarcasm detection}},
  author = {Potamias, Rolandos Alexandros and Siolas, Georgios and Stafylopatis, Andreas - Georgios},
  year = {2019},
  doi = {10.1007/s00521-020-05102-3},
  eprint = {1911.10401},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.1007/s00521-020-05102-3}
}

References

104 references, 17 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

  1. Amir, S., Wallace, B.C., Lyu, H., Silva, P.C.M.J.: Modelling context with user embeddings for sarcasm detection in social media. arXiv preprint arXiv:1607.00976 (2016) ↗ our card
  2. Antonakaki, D., Spiliotopoulos, D., V. Samaras, C., Pratikakis, P., Ioannidis, S., Fragopoulou, P.: Social media analysis during political turbulence. PLOS ONE 12(10) (2017) find
  3. Barbieri, F., Ronzano, F., Saggion, H.: UPF-taln: SemEval 2015 Tasks 10 and 11. Sentiment Analysis of Literal and Figurative Language in Twitter. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 704–708. Association for Computational Linguistics, Denver, Colorado (2015) find
  4. Barbieri, F., Saggion, H.: Modelling Irony in Twitter. In: EACL (2014) find
  5. Baziotis, C., Nikolaos, A., Papalampidi, P., Kolovou, A., Paraskevopoulos, G., Ellinas, N., Potamianos, A.: NTUA-SLP at SemEval-2018 Task 3: Tracking Ironic Tweets using Ensembles of Word and Character Level Attentive RNNs. In: Proceedings of The 12th International Workshop on Semantic Evaluation, pp. 613– 621. Association for Computational Linguistics, New Orleans, Louisiana (2018) find
  6. Benedek, M., Beaty, R., Jauk, E., Koschutnig, K., Fink, A., Silvia, P.J., Dunst, B., Neubauer, A.C.: Creating metaphors: The neural basis of figurative language production. NeuroImage 90, 99–106 (2014) find
  7. Buschmeier, K., Cimiano, P., Klinger, R.: An impact analysis of features in a classification approach to irony detection in product reviews. In: Proceedings of the 5th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pp. 42–49. Association for Computational Linguistics, Baltimore, Maryland (2014) find
  8. Carvalho, P.: Clues for detecting irony in user-generated contents: Oh...!! its so easy. In: International CIKM Workshop on Topic-Sentiment Analysis for Mass Opinion Measurement, Hong Kong (2009) find
  9. Cer, D., Yang, Y., Kong, S.y., Hua, N., Limtiaco, N., John, R.S., Constant, N., Guajardo-Cespedes, M., Yuan, S., Tar, C., et al.: Universal sentence encoder. arXiv preprint arXiv:1803.11175 (2018) ↗ our card
  10. Charalampakis, B., Spathis, D., Kouslis, E., Kermanidis, K.: A comparison between semi-supervised and supervised text mining techniques on detecting irony in greek political tweets. Engineering Applications of Artificial Intelligence 51, 50–57 (2016) find
  11. Chelba, C., Mikolov, T., Schuster, M., Ge, Q., Brants, T., Koehn, P., Robinson, T.: One billion word benchmark for measuring progress in statistical language modeling. arXiv preprint arXiv:1312.3005 (2013) ↗ our card
  12. Clark, H.H., Gerrig, R.J.: On the pretense theory of irony. (1984) find
  13. Cuccio, V., Ambrosecchia, M., Ferri, F., Carapezza, M., Piparo, F.L., Fogassi, L., Gallese, V.: How the context matters. Literal and figurative meaning in the embodied language paradigm. PLoS ONE 9(12) (2014) find
  14. Dai, A.M., Le, Q.V.: Semi-supervised sequence learning. In: Advances in neural information processing systems, pp. 3079–3087 (2015) find
  15. Dai, Z., Yang, Z., Yang, Y., Cohen, W.W., Carbonell, J., Le, Q.V., Salakhutdinov, R.: Transformer-xl: Attentive language models beyond a fixed-length context. arXiv preprint arXiv:1901.02860 (2019) ↗
  16. Davidov, D., Tsur, O., Rappoport, A.: Semi-supervised Recognition of Sarcastic Sentences in Twitter and Amazon. In: Proceedings of the Fourteenth Conference on Computational Natural Language Learning, CoNLL ’10, pp. 107–116. Association for Computational Linguistics, Stroudsburg, PA, USA (2010) find our card
  17. Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: 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), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019) find
  18. Dridi, A., Recupero, D.R.: Leveraging semantics for sentiment polarity detection in social media. International Journal of Machine Learning and Cybernetics 10(8), 2045–2055 (2019) find
  19. Dubey, A., Kumar, L., Somani, A., Joshi, A., Bhattacharyya, P.: when numbers matter!: Detecting sarcasm in numerical portions of text. In: Proceedings of the Tenth Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pp. 72–80 (2019) find
  20. Farı́as, D.I.H., Montes-y Gómez, M., Escalante, H.J., Rosso, P., Patti, V.: A knowledge-based weighted knn for detecting irony in twitter. In: Mexican International Conference on Artificial Intelligence, pp. 194–206. Springer (2018) find
  21. Farı́as, D.I.H., Patti, V., Rosso, P.: Irony detection in twitter: The role of affective content. ACM Transactions on Internet Technology (TOIT) 16(3), 19 (2016) find
  22. Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., Lempitsky, V.: Domain-adversarial training of neural networks. The Journal of Machine Learning Research 17(1), 2096– 2030 (2016) find
  23. Gao, Z., Gao, S., Xu, L., Zheng, X., Ma, X., Luo, L., Kendrick, K.M.: Women prefer men who use metaphorical language when paying compliments in a romantic context. Scientific Reports 7, 40871 (2017) find
  24. Ghosh, A., Li, G., Veale, T., Rosso, P., Shutova, E., Barnden, J., Reyes, A.: SemEval-2015 task 11: Sentiment analysis of figurative language in twitter. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 470–478. Association for Computational Linguistics, Denver, Colorado (2015) find
  25. Ghosh, A., Veale, T.: Fracking sarcasm using neural network. In: Proceedings of the 7th workshop on computational approaches to subjectivity, sentiment and social media analysis, pp. 161–169 (2016) find our card
  26. Ghosh, D., Guo, W., Muresan, S.: Sarcastic or Not: Word Embeddings to Predict the Literal or Sarcastic Meaning of Words. In: EMNLP (2015) find
  27. Gimnez, M., Pla, F., Hurtado, L.F.: ELiRF: A SVM Approach for SA tasks in Twitter at SemEval-2015. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 574–581. Association for Computational Linguistics, Denver, Colorado (2015) find
  28. Gonzlez-Ibez, R.I., Muresan, S., Wacholder, N.: Identifying Sarcasm in Twitter: A Closer Look. In: ACL (2011) find our card
  29. Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press (2016) find
  30. Grice, H.P.: Further Notes on Logic and Conversation. In: J.E. Adler, L.J. Rips (eds.) Reasoning: Studies of Human Inference and its Foundations, pp. 765–773. Cambridge University Press, Cambridge (2008) find
  31. Gupta, U., Chatterjee, A., Srikanth, R., Agrawal, P.: A Sentiment-and-Semantics-Based Approach for Emotion Detection in Textual Conversations. (2017) find
  32. Hangya, V., Farkas, R.: A comparative empirical study on social media sentiment analysis over various genres and languages. Artificial Intelligence Review 47(4), 485–505 (2017) find
  33. Hazarika, D., Poria, S., Gorantla, S., Cambria, E., Zimmermann, R., Mihalcea, R.: Cascade: Contextual sarcasm detection in online discussion forums. arXiv preprint arXiv:1805.06413 (2018) ↗ our card
  34. Hee, C.V., Lefever, E., Hoste, V.: SemEval-2018 Task 3: Irony Detection in English Tweets. In: SemEval@NAACL-HLT (2018) find our card
  35. Hiai, S., Shimada, K.: Sarcasm detection using features based on indicator and roles. In: International Conference on Soft Computing and Data Mining, pp. 418–428. Springer (2018) find
  36. Howard, J., Ruder, S.: Universal language model fine-tuning for text classification. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 328–339. Association for Computational Linguistics, Melbourne, Australia (2018) find
  37. Howard, J., Ruder, S.: Universal language model fine-tuning for text classification. arXiv preprint arXiv:1801.06146 (2018) ↗
  38. Huang, Y.H., Huang, H.H., Chen, H.H.: Irony Detection with Attentive Recurrent Neural Networks. In: ECIR (2017) find
  39. Ilić, S., Marrese-Taylor, E., Balazs, J.A., Matsuo, Y.: Deep contextualized word representations for detecting sarcasm and irony. arXiv preprint arXiv:1809.09795 (2018) ↗
  40. Iyyer, M., Manjunatha, V., Boyd-Graber, J., Daumé III, H.: Deep unordered composition rivals syntactic methods for text 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 1: Long Papers), pp. 1681–1691. Association for Computational Linguistics, Beijing, China (2015) find
  41. Jianqiang, Z., Xiaolin, G., Xuejun, Z.: Deep Convolution Neural Networks for Twitter Sentiment Analysis. IEEE Access (2018) find
  42. Joseph, J.K., Dev, K.A., Pradeepkumar, A.P., Mohan, M.: Chapter 16 - Big Data Analytics and Social Media in Disaster Management. In: P. Samui, D. Kim, C.B.T.I.D.S. Ghosh, Management (eds.) Integrating Disaster Science and Management, pp. 287–294. Elsevier (2018) find
  43. Joshi, M., Chen, D., Liu, Y., Weld, D.S., Zettlemoyer, L., Levy, O.: Spanbert: Improving pre-training by representing and predicting spans. arXiv preprint arXiv:1907.10529 (2019) ↗ our card
  44. Joulin, A., Grave, E., Bojanowski, P., Douze, M., Jégou, H., Mikolov, T.: Fasttext. zip: Compressing text classification models. arXiv preprint arXiv:1612.03651 (2016) ↗ our card
  45. Kasparian, K.: Hemispheric differences in figurative language processing: Contributions of neuroimaging methods and challenges in reconciling current empirical findings (2013) find
  46. Katz, J.J.: Propositional structure and illocutionary force : a study of the contribution of sentence meaning to speech acts / Jerrold J. Katz. The Language and thought series. Crowell, New York (1977) find
  47. Khodak, M., Saunshi, N., Vodrahalli, K.: A Large Self-Annotated Corpus for Sarcasm. ArXiv e-prints (2017) find our card
  48. Kim, E., Klinger, R.: A Survey on Sentiment and Emotion Analysis for Computational Literary Studies. (2018) find
  49. Kingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. ArXiv e-prints (2014) find
  50. Kumar, A., Garg, G.: Empirical study of shallow and deep learning models for sarcasm detection using context in benchmark datasets. Journal of Ambient Intelligence and Humanized Computing pp. 1–16 (2019) find
  51. Kumar, A., Sangwan, S.R., Arora, A., Nayyar, A., Abdel-Basset, M., et al.: Sarcasm detection using soft attention-based bidirectional long short-term memory model with convolution network. IEEE Access 7, 23319– 23328 (2019) find
  52. Kumar, L., Somani, A., Bhattacharyya, P.: Having 2 hours to write a paper is fun!: Detecting Sarcasm in Numerical Portions of Text. ArXiv e-prints (2017) find
  53. Lai, S., Xu, L., Liu, K., Zhao, J.: Recurrent convolutional neural networks for text classification. In: Twenty-ninth AAAI conference on artificial intelligence (2015) find
  54. Lample, G., Conneau, A.: Cross-lingual language model pretraining. arXiv preprint arXiv:1901.07291 (2019) ↗
  55. Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabasi, A.L., Brewer, D., Christakis, N., Contractor, N., Fowler, J., Gutmann, M., Jebara, T., King, G., Macy, M., Roy, D., Van Alstyne, M.: Life in the network: the coming age of computational social science. Science (New York, N.Y.) 323(5915), 721–723 (2009) find
  56. Ling, J., Klinger, R.: An empirical, quantitative analysis of the differences between sarcasm and irony. In: European Semantic Web Conference, pp. 203–216. Springer (2016) find
  57. Liu, B.: Sentiment Analysis - Mining Opinions, Sentiments, and Emotions. Cambridge University Press (2015) find
  58. Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., Stoyanov, V.: Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019) ↗
  59. Loenneker-Rodman, B., Narayanan, S.: Computational approaches to figurative language. Cambridge Encyclopedia of Psycholinguistics. (2010) find
  60. McCann, B., Bradbury, J., Xiong, C., Socher, R.: Learned in translation: Contextualized word vectors. In: Advances in Neural Information Processing Systems, pp. 6294–6305 (2017) find our card
  61. Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient Estimation of Word Representations in Vector Space. ArXiv e-prints (2013) find
  62. Mikolov, T., Sutskever, I., Chen, K., Corrado, G., Dean, J.: Distributed Representations of Words and Phrases and their Compositionality. ArXiv e-prints (2013) find
  63. Montgomery, D.C.: Design and Analysis of Experiments, ninth edition edn. John Wiley & Sons (2017) find
  64. Nguyen, T.H., Grishman, R.: Relation extraction: Perspective from convolutional neural networks. In: Proceedings of the 1st Workshop on Vector Space Modeling for Natural Language Processing, pp. 39–48. Association for Computational Linguistics, Denver, Colorado (2015) find
  65. Nozza, D., Fersini, E., Messina, E.: Unsupervised irony detection: A probabilistic model with word embeddings. In: KDIR, pp. 68–76 (2016) find
  66. Oboler, A., Welsh, K., Cruz, L.: The danger of big data: Social media as computational social science. First Monday 17(7) (2012) find
  67. Ortega-Bueno, R., Rangel, F., Hernández Farıas, D., Rosso, P., Montes-y Gómez, M., Medina Pagola, J.E.: Overview of the task on irony detection in spanish variants. In: Proceedings of the Iberian Languages Evaluation Forum (IberLEF 2019), co-located with 34th Conference of the Spanish Society for Natural Language Processing (SEPLN 2019). CEUR-WS. org (2019) find
  68. Pennebaker, J., Francis, M.: Linguistic Inquiry and Word Count. Lawrence Erlbaum Associates, Incorporated (1999) find
  69. Pennington, J., Socher, R., Manning, C.D.: Glove: Global Vectors for Word Representation. In: EMNLP, vol. 14, pp. 1532–1543 (2014) find
  70. Peters, M.E., Ammar, W., Bhagavatula, C., Power, R.: Semi-supervised sequence tagging with bidirectional language models. arXiv preprint arXiv:1705.00108 (2017) ↗ our card
  71. Peters, M.E., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., Zettlemoyer, L.: Deep contextualized word representations. arXiv preprint arXiv:1802.05365 (2018) ↗
  72. Potamias, R.A., Neofytou, A., Siolas, G.: NTUA-ISLab at SemEval-2019 task 9: Mining suggestions in the wild. In: Proceedings of the 13th International Workshop on Semantic Evaluation, pp. 1224–1230. Association for Computational Linguistics, Minneapolis, Minnesota, USA (2019) find
  73. Potamias, R.A., Siolas, G.: NTUA-ISLab at SemEval-2019 Task 3: Determining emotions in contextual conversations with deep learning. In: Proceedings of the 13th International Workshop on Semantic Evaluation, pp. 277–281. Association for Computational Linguistics, Minneapolis, Minnesota, USA (2019) find
  74. Potamias, R.A., Siolas, G., Stafylopatis, A.: A robust deep ensemble classifier for figurative language detection. In: International Conference on Engineering Applications of Neural Networks, pp. 164–175. Springer (2019) find
  75. Radford, A., Narasimhan, K., Salimans, T., Sutskever, I.: Improving language understanding by generative pre-training (2018) find
  76. Rajadesingan, A., Zafarani, R., Liu, H.: Sarcasm Detection on Twitter: A Behavioral Modeling Approach. In: WSDM (2015) find
  77. Ravi, K., Ravi, V.: A novel automatic satire and irony detection using ensembled feature selection and data mining. Knowledge-Based Systems 120, 15 – 33 (2017) find
  78. Reyes, A., Rosso, P., Buscaldi, D.: From humor recognition to irony detection: The figurative language of social media. Data & Knowledge Engineering 74, 1 – 12 (2012) find
  79. Reyes, A., Rosso, P., Veale, T.: A Multidimensional Approach for Detecting Irony in Twitter. Lang. Resour. Eval. 47(1), 239–268 (2013) find
  80. Riloff, E., Qadir, A., Surve, P., De Silva, L., Gilbert, N., Huang, R.: Sarcasm as contrast between a positive sentiment and negative situation. In: EMNLP 2013 - 2013 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference, pp. 704– 714. Association for Computational Linguistics (ACL) (2013) find our card
  81. Rosenthal, S., Ritter, A., Nakov, P., Stoyanov, V.: SemEval-2014 task 9: Sentiment analysis in twitter. In: Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 73–80. Association for Computational Linguistics, Dublin, Ireland (2014) find
  82. Singh, N.K., Tomar, D.S., Sangaiah, A.K.: Sentiment analysis: a review and comparative analysis over social media. Journal of Ambient Intelligence and Humanized Computing (2018) find
  83. Sperber, D., Wilson, D.: Irony and the use-mention distinction. Radical Pragmatics (1981) find
  84. Stranisci, M., Bosco, C., FARIAS, H., IRAZU, D., Patti, V.: Annotating sentiment and irony in the online italian political debate on# labuonascuola. In: Tenth International Conference on Language Resources and Evaluation LREC 2016,, pp. 2892–2899. elra (2016) find
  85. Sulis, E., Farı́as, D.I.H., Rosso, P., Patti, V., Ruffo, G.: Figurative messages and affect in twitter: Differences between# irony,# sarcasm and# not. Knowledge-Based Systems 108, 132–143 (2016) find
  86. Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in neural information processing systems, pp. 3104–3112 (2014) find
  87. Tay, Y., Luu, A.T., Hui, S.C., Su, J.: Reasoning with sarcasm by reading in-between. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 1010– 1020. Association for Computational Linguistics, Melbourne, Australia (2018) find our card
  88. Van Hee, C., Lefever, E., Hoste, V.: LT3: Sentiment Analysis of Figurative Tweets: piece of cake #NotReally. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 684–688. Association for Computational Linguistics, Denver, Colorado (2015) find
  89. Van Hee, C., Lefever, E., Hoste, V.: Exploring the fine-grained analysis and automatic detection of irony on twitter. Language Resources and Evaluation 52(3), 707–731 (2018) find
  90. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Advances in neural information processing systems, pp. 5998–6008 (2017) find
  91. W. Gibbs, R.: On the psycholinguistics of sarcasm. Journal of Experimental Psychology: General 115 (1986) find
  92. Wallace, B.C., Choe, D.K., Charniak, E.: Sparse, contextually informed models for irony detection: Exploiting user communities, entities and sentiment. In: ACL-IJCNLP 2015 - 53rd Annual Meeting of the Association for Computational Linguistics (ACL), Proceedings of the Conference, vol. 1 (2015) find
  93. Wang, S., Manning, C.D.: Baselines and bigrams: Simple, good sentiment and topic classification. In: Proceedings of the 50th annual meeting of the association for computational linguistics: Short papers-volume 2, pp. 90–94. Association for Computational Linguistics (2012) find
  94. Weiland, H., Bambini, V., Schumacher, P.B.: The role of literal meaning in figurative language comprehension: evidence from masked priming ERP. Frontiers in Human Neuroscience 8 (2014) find
  95. Winbey, J.P.: The social fact. The MIT Press, Cambridge, Masschussets (2019) find
  96. Wu, C., Wu, F., Wu, S., Liu, J., Yuan, Z., Huang, Y.: THU ngn at SemEval-2018 Task 3: Tweet Irony Detection with Densely connected LSTM and Multi-task Learning. In: SemEval@NAACL-HLT (2018) find
  97. Wu, Y., Schuster, M., Chen, Z., Le, Q.V., Norouzi, M., Macherey, W., Krikun, M., Cao, Y., Gao, Q., Macherey, K., et al.: Google’s neural machine translation system: Bridging the gap between human and machine translation. arXiv preprint arXiv:1609.08144 (2016) ↗
  98. Xu, H., Santus, E., Laszlo, A., Huang, C.R.: LLT-PolyU: Identifying Sentiment Intensity in Ironic Tweets. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 673–678. Association for Computational Linguistics, Denver, Colorado (2015) find
  99. Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R., Le, Q.V.: Xlnet: Generalized autoregressive pretraining for language understanding. arXiv preprint arXiv:1906.08237 (2019) ↗ our card
  100. You, Y., Li, J., Hseu, J., Song, X., Demmel, J., Hsieh, C.J.: Reducing bert pre-training time from 3 days to 76 minutes. arXiv preprint arXiv:1904.00962 (2019) ↗
  101. Zhang, S., Zhang, X., Chan, J., Rosso, P.: Irony detection via sentiment-based transfer learning. Information Processing & Management 56(5), 1633–1644 (2019) find
  102. Zhou, L., Pan, S., Wang, J., Vasilakos, A.V.: Machine learning on big data: Opportunities and challenges. Neurocomputing 237, 350–361 (2017) find
  103. Zhu, Y., Kiros, R., Zemel, R., Salakhutdinov, R., Urtasun, R., Torralba, A., Fidler, S.: Aligning books and movies: Towards story-like visual explanations by watching movies and reading books. In: Proceedings of the IEEE international conference on computer vision, pp. 19–27 (2015) find our card
  104. zdemir, C., Bergler, S.: CLaC-SentiPipe: SemEval2015 Subtasks 10 B,E, and Task 11. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), pp. 479–485. Association for Computational Linguistics, Denver, Colorado (2015) find