Humans Require Context to Infer Ironic Intent (so Computers Probably do, too)

By Byron C. Wallace, Do Kook Choe, Laura Kertz, Eugene Charniak. 2014

145 citations recorded by the source corpus.

Automatically detecting verbal irony (roughly, sarcasm) is a challenging task because ironists say something other than ‐ and often opposite to ‐ what they actually mean. Discerning ironic intent exclusively from the words and syntax comprising texts (e.g., tweets, forum posts) is therefore not always possible: additional contextual information about the speaker and/or the topic at hand is often necessary. We introduce a new corpus that provides empirical evidence for this claim. We show that annotators frequently require context to make judgements concerning ironic intent, and that machine learning approaches tend to misclassify those same comments for which annotators required additional context.

Subfields: Artificial Intelligence

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Citations in the corpus

A Sensitivity Analysis of (and Practitioners’ Guide to) Convolutional Neural Networks for Sentence Classification (2015, background): “(9) Irony (Wallace et al., 2014): this contains 16,006 sentences from reddit labeled as ironic (or not).”
The Importance of Modeling Social Factors of Language: Theory and Practice (2021, background): “, 2016) and the detection of irony (Wallace et al., 2014) and sarcasm (Abercrombie and Hovy, 2016).”
An extensive review of tools for manual annotation of documents (2019, methodology): “[4]) or used a custom (private) tool or an adapted (non-available) version of an existing one [12, 13].”
Deep Learning--based Text Classification (2020, methodology): “lassification includes PubMed 200k RCT [194], Irony (which is composed of annotated comments from the social news website reddit, Twitter dataset for topic classification of tweets, arXiv collection) [195], to name a few. 4.4 QA Datasets SQuAD. Stanford Question Answering Dataset (SQuAD) [24] is a c”
MGNC-CNN: A Simple Approach to Exploiting Multiple Word Embeddings for Sentence Classification (2016, methodology): “Two datasets are derived from this corpus: (1) SST-1 , containing et al., 2014).”
Misogyny Detection in Twitter: a Multilingual and Cross-Domain Study (2020, unknown): “Moreover, ex-ternal world knowledge is often required in order to infer whether an utterance is ironic Wallace, Kertz, Charniak et al. (2014).”
Towards Multimodal Sarcasm Detection (An _Obviously_ Perfect Paper) (2019, background): “Other research leverages context to acquire shared knowledge between the speaker and the audience (Wallace et al., 2014; Bamman and Smith, 2015).”
Contextualized Sarcasm Detection on Twitter (2015, background): “For example, humans require access to the surrounding context in which a Reddit post was written (such as the thread it appears in) in order to judge its tone (Wallace et al. 2014).”