Deep contextualized word representations for detecting sarcasm and irony

Predicting context-dependent and non-literal utterances like sarcastic and ironic expressions still remains a challenging task in NLP, as it goes beyond linguistic patterns, encompassing common sense and shared knowledge as crucial components. To capture complex morpho-syntactic features that can usually serve as indicators for irony or sarcasm across dynamic contexts, we propose a model that uses character-level vector representations of words, based on ELMo. We test our model on 7 different datasets derived from 3 different data sources, providing state-of-the-art performance in 6 of them, and otherwise offering competitive results.

[1]  Thorsten Brants,et al.  One billion word benchmark for measuring progress in statistical language modeling , 2013, INTERSPEECH.

[2]  Sanjay Kumar Jena,et al.  Parsing-based sarcasm sentiment recognition in Twitter data , 2015, 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM).

[3]  Brendan T. O'Connor,et al.  Part-of-Speech Tagging for Twitter: Annotation, Features, and Experiments , 2010, ACL.

[4]  Luke S. Zettlemoyer,et al.  Deep Contextualized Word Representations , 2018, NAACL.

[5]  Cameron Shelley,et al.  The bicoherence theory of situational irony , 2001, Cogn. Sci..

[6]  Sanjay Kumar Jena,et al.  Sarcastic sentiment detection in tweets streamed in real time: a big data approach , 2016, Digit. Commun. Networks.

[7]  Elena Filatova,et al.  Irony and Sarcasm: Corpus Generation and Analysis Using Crowdsourcing , 2012, LREC.

[8]  Jian Su,et al.  Reasoning with Sarcasm by Reading In-Between , 2018, ACL.

[9]  Joan Lucariello.  Situational irony: A concept of events gone awry. , 1994 .

[10]  Pushpak Bhattacharyya,et al.  Automatic Sarcasm Detection , 2016, ACM Comput. Surv..

[11]  Jun Hong,et al.  Sarcasm Detection on Czech and English Twitter , 2014, COLING.

[12]  Mikhail Khodak,et al.  A Large Self-Annotated Corpus for Sarcasm , 2017, LREC.

[13]  Pushpak Bhattacharyya,et al.  Harnessing Context Incongruity for Sarcasm Detection , 2015, ACL.

[14]  Byron C. Wallace.  Computational irony: A survey and new perspectives , 2013, Artificial Intelligence Review.

[15]  Véronique Hoste,et al.  SemEval-2018 Task 3: Irony Detection in English Tweets , 2018, *SEMEVAL.

[16]  Saif Mohammad,et al.  SemEval-2018 Task 1: Affect in Tweets , 2018, *SEMEVAL.

[17]  Paolo Rosso,et al.  A multidimensional approach for detecting irony in Twitter , 2013, Lang. Resour. Evaluation.

[18]  Antal van den Bosch,et al.  Signaling sarcasm: From hyperbole to hashtag , 2015, Inf. Process. Manag..

[19]  Chaitali Chandankhede,et al.  Literature survey of sarcasm detection , 2017, 2017 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET).

[20]  Navneet Kaur,et al.  Opinion mining and sentiment analysis , 2016, 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom).

[21]  Marilyn A. Walker,et al.  Creating and Characterizing a Diverse Corpus of Sarcasm in Dialogue , 2016, SIGDIAL Conference.

[22]  Sebastian Ruder,et al.  Universal Language Model Fine-tuning for Text Classification , 2018, ACL.

[23]  Siobhan Chapman.  Logic and Conversation , 2005 .

[24]  Yue Zhang,et al.  Tweet Sarcasm Detection Using Deep Neural Network , 2016, COLING.

[25]  Masnizah Mohd,et al.  Recognition of Sarcasm in Microblogging Based on Sentiment Analysis and Coherence Identification , 2016 .

[26]  Ellen Riloff,et al.  Sarcasm as Contrast between a Positive Sentiment and Negative Situation , 2013, EMNLP.