Reasoning with Sarcasm by Reading In-Between

Abstract

Sarcasm is a sophisticated speech act which commonly manifests on social communities such as Twitter and Reddit. The prevalence of sarcasm on the social web is highly disruptive to opinion mining systems due to not only its tendency of polarity flipping but also usage of figurative language. Sarcasm commonly manifests with a contrastive theme either between positive-negative sentiments or between literal-figurative scenarios. In this paper, we revisit the notion of modeling contrast in order to reason with sarcasm. More specifically, we propose an attention-based neural model that looks inbetween instead of across, enabling it to explicitly model contrast and incongruity. We conduct extensive experiments on six benchmark datasets from Twitter, Reddit and the Internet Argument Corpus. Our proposed model not only achieves stateof-the-art performance on all datasets but also enjoys improved interpretability.

Exploring Author Context for Detecting Intended vs Perceived Sarcasm

“…As a baseline, we implement the SIARN (Single-Dimension Intra-Attention Network) model proposed by (Tay et al, 2018), since it achieves the best published results on both our datasets. SIARN only looks at the tweet being classified, that is SIARN(t, e t ) = m (t).…”

A deep-learning framework to detect sarcasm targets

“…They observe mainly three approaches to the sarcasm detection problem -semi-supervised extraction of sarcastic patterns Ptáček et al, 2014;Bouazizi and Ohtsuki, 2015;Riloff et al, 2013;Joshi et al, 2015), use of hashtag based supervision Abercrombie and Hovy, 2016), and use of contextual information for sarcasm detection (Hazarika et al, 2018;Wallace et al, 2014;Rajadesingan et al, 2015). Recently, Tay et al (2018) presented an attention-based neural model to explicitly model contrast and incongruity. Kolchinski and Potts (2018) presented two methods for representing authors in the context of textual sarcasm detection; they show that augmenting a bidirectional RNN with these representations improves performance in sarcasm detection.…”

Attentive Gated Lexicon Reader with Contrastive Contextual Co-Attention for Sentiment Classification

“…The winning solution of SemEval 2016 (Deriu et al, 2016) utilized ensembles of convolutional neural networks (CNN). Recurrent-based models such as the bidirectional long short-term memory (BiLSTM) (Hochreiter and Schmidhuber, 1997;Graves et al, 2013) are popular and standard strong baselines for many opinion mining tasks including sentiment analysis (Tay et al, 2017) and sarcasm detection (Tay et al, 2018c). These neural models such as the BiLSTM are capable of modeling semantic compositionality and produce a feature vector which can be used for classification.…”

“…In our work, we adapt this to model the similarities between (1) lexicon-context and (2) contrasting polarities which borrows inspiration from (Riloff et al, 2013). Since our matching problem is derived from the same sequence (identified by a lexicon prior), this work can be interpreted as a form of self-attention (Vaswani et al, 2017) which draws relations to the intra-attentive model for sarcasm detection (Tay et al, 2018c).…”