Sarcasm SIGN: Interpreting Sarcasm with Sentiment Based Monolingual Machine Translation

Sarcasm is a form of speech in which speakers say the opposite of what they truly mean in order to convey a strong sentiment. In other words, “Sarcasm is the giant chasm between what I say, and the person who doesn’t get it.”. In this paper we present the novel task of sarcasm interpretation, defined as the generation of a non-sarcastic utterance conveying the same message as the original sarcastic one. We introduce a novel dataset of 3000 sarcastic tweets, each interpreted by five human judges. Addressing the task as monolingual machine translation (MT), we experiment with MT algorithms and evaluation measures. We then present SIGN: an MT based sarcasm interpretation algorithm that targets sentiment words, a defining element of textual sarcasm. We show that while the scores of n-gram based automatic measures are similar for all interpretation models, SIGN’s interpretations are scored higher by humans for adequacy and sentiment polarity. We conclude with a discussion on future research directions for our new task.

[1]  David Bamman,et al.  Contextualized Sarcasm Detection on Twitter , 2015, ICWSM.

[2]  Nina Wacholder,et al.  Identifying Sarcasm in Twitter: A Closer Look , 2011, ACL.

[3]  Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing , EMNLP 2004, A meeting of SIGDAT, a Special Interest Group of the ACL, held in conjunction with ACL 2004, 25-26 July 2004, Barcelona, Spain , 2004, EMNLP.

[4]  M. Nissim,et al.  Proceedings of the 4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis , 2013 .

[5]  Hermann Ney,et al.  A Systematic Comparison of Various Statistical Alignment Models , 2003, CL.

[6]  Stephen Cox,et al.  Some statistical issues in the comparison of speech recognition algorithms , 1989, International Conference on Acoustics, Speech, and Signal Processing,.

[7]  Salim Roukos,et al.  Bleu: a Method for Automatic Evaluation of Machine Translation , 2002, ACL.

[8]  William B. Dolan,et al.  Collecting Highly Parallel Data for Paraphrase Evaluation , 2011, ACL.

[9]  Omer Levy,et al.  Dependency-Based Word Embeddings , 2014, ACL.

[10]  Ari Rappoport,et al.  ICWSM - A Great Catchy Name: Semi-Supervised Recognition of Sarcastic Sentences in Online Product Reviews , 2010, ICWSM.

[11]  Matthew D. Zeiler.  ADADELTA: An Adaptive Learning Rate Method , 2012, ArXiv.

[12]  Andrea Esuli,et al.  SENTIWORDNET: A Publicly Available Lexical Resource for Opinion Mining , 2006, LREC.

[13]  J Aharon-Peretz,et al.  The neuroanatomical basis of understanding sarcasm and its relationship to social cognition. , 2005, Neuropsychology.

[14]  Chris Callison-Burch,et al.  SemEval-2015 Task 1: Paraphrase and Semantic Similarity in Twitter (PIT) , 2015, *SEMEVAL.

[15]  Po-Ya Angela Wang.  #Irony or #Sarcasm — A Quantitative and Qualitative Study Based on Twitter , 2013, PACLIC.

[16]  Philipp Koehn,et al.  Moses: Open Source Toolkit for Statistical Machine Translation , 2007, ACL.

[17]  Ari Rappoport,et al.  Semi-Supervised Recognition of Sarcasm in Twitter and Amazon , 2010, CoNLL.

[18]  Virginia Slaughter,et al.  The mind behind the message: advancing theory-of-mind scales for typically developing children, and those with deafness, autism, or Asperger syndrome. , 2012, Child development.

[19]  Janyce Wiebe,et al.  Learning Subjective Language , 2004, CL.

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

[21]  Daniel Marcu,et al.  Statistical Phrase-Based Translation , 2003, NAACL.

[22]  Quoc V. Le,et al.  Sequence to Sequence Learning with Neural Networks , 2014, NIPS.

[23]  Noah Webster,et al.  Webster's ninth new collegiate dictionary , 2012 .

[24]  Andrés Montoyo,et al.  Proceedings of the 4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis , 2013 .

[25]  George R. Doddington,et al.  Automatic Evaluation of Machine Translation Quality Using N-gram Co-Occurrence Statistics , 2002 .

[26]  Alon Lavie,et al.  Meteor 1.3: Automatic Metric for Reliable Optimization and Evaluation of Machine Translation Systems , 2011, WMT@EMNLP.

[27]  George A. Miller,et al.  Introduction to WordNet: An On-line Lexical Database , 1990 .

[28]  Herbert L. Colston,et al.  Irony in Language and Thought : A Cognitive Science Reader , 2007 .

[29]  Emiel Krahmer,et al.  Paraphrase Generation as Monolingual Translation: Data and Evaluation , 2010, INLG.

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

[31]  F. Stringfellow.  The meaning of irony , 1994 .

[32]  A. Kaplan,et al.  Two hearts in three-quarter time: How to waltz the social media/viral marketing dance , 2011 .

[33]  C. Colebrook.  The meaning of irony , 2000 .

[34]  D. Maynard,et al.  Challenges in developing opinion mining tools for social media , 2012 .

[35]  Chris Callison-Burch,et al.  Paraphrasing with Bilingual Parallel Corpora , 2005, ACL.

[36]  Oren Etzioni,et al.  OPINE: Extracting Product Features and Opinions from Reviews , 2005, HLT/EMNLP.

[37]  Yael Kimhi.  Theory of Mind Abilities and Deficits in Autism Spectrum Disorders , 2014 .

[38]  Kenneth Heafield,et al.  KenLM: Faster and Smaller Language Model Queries , 2011, WMT@EMNLP.

[39]  Diana Maynard,et al.  Who cares about Sarcastic Tweets? Investigating the Impact of Sarcasm on Sentiment Analysis. , 2014, LREC.

[40]  D. Muecke.  Irony and the Ironic , 1970 .

[41]  Yoshua Bengio,et al.  Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation , 2014, EMNLP.