A Dual-Channel Framework for Sarcasm Recognition by Detecting Sentiment Conflict (2022)

Abstract: Sarcasm employs ambivalence, where one says something positive but actually means negative, and vice versa. The essence of sarcasm, which is also a sufficient and necessary condition, is the conflict between literal and implied sentiments expressed in one sentence. However, it is difficult to recognize such sentiment conflict because the sentiments are mixed or even implicit. As a result, the recognition of sophisticated and obscure sentiment brings in a great challenge to sarcasm detection. In this paper, we propose a Dual-Channel Framework by modeling both literal and implied sentiments separately. Based on this dual-channel framework, we design the Dual-Channel Network (DC-Net) to recognize sentiment conflict. Experiments on political debates (i.e. IAC-V1 and IAC-V2) and Twitter datasets show that our proposed DC-Net achieves state-of-the-art performance on sarcasm recognition. Our code is released to support research.

## introduction sarcasm is a complicated linguistic phenomenon. intuitively, it means that one says something positive on surface form, while he/she actually expresses negative, vice versa @xcite . take the sentence "final exam is the best gift on my birthday" as an example, the literal sentiment on surface is positive, which is reflected by the explicit sentiment words, i.e., "best gift". however, the factual part of the text (i.e., "final exam happens on birthday") implies that the sentiment expressed is negative. this example suggests *indicates equal contribution 1 https://github.com/yiyi-ict/ dual-channel-for-sarcasm that it is the sentiment conflict that causes sarcasm linguistically. however, modeling this linguistic nature of sarcasm is a great challenge due to the difficulty of digging sentiment conflict between the literal and the implied meanings. we know that non-sarcastic texts do not contain implied meaning, so the literal sentiment is consistent with the actual sentiment. but for sarcastic text, there is more than one meaning that coexists in one sentence. the literal meaning and the implied meaning are reflected in different sub-sentences. even more challenging, sentiments behind the two meanings are mixed or even implicit. many existing studies adopt generic classification models for sarcasm recognition @xcite . however, these methods directly model the entire sentence without considering the contradictory meanings behind sarcastic texts. there are also studies using contrast patterns (e.g., phrase pair and word pair) as indicators to detect sarcasm, which is approaching the linguistic essence of sarcasm. @xcite ; @xcite detect contrast or incongruity patterns, i.e., the co-occurrence of positive sentiment phrases and negative situational phrases. @xcite ; @xcite use attention mechanism to measure the sentiment conflict between word pairs in sarcastic texts. however, these methods emphasize too much on the explicit sentiment conflict on surface form (i.e., word/phrase level), which mainly reflect the literal meaning. as a result, the factual text is underestimated, which expresses the implied sentiment. dual-channel framework. in this paper, we propose a dual-channel framework to model the literal sentiment and the implied sentiment simultaneously. this allows us to leverage the conflict between the two channels in a comprehensive way. figure 1 depicts the proposed dual-channel framework. in this framework, literal channel and implied channel are used to detect the surface and the hidden meanings separately. once sentiment conflict is detected, we could determine the existence of sarcasm. the design of our dual-channel framework balances the effect of literal and implied inputs and avoids focusing too much on either one channel while ignoring the other. our framework covers existing sarcasm patterns, and could be further enhanced to detect more sentiment conflict patterns. based on this framework, we develop the dual-channel network (dc-net) to detect sarcasm. dc-net contains four modules: decomposer, literal channel, implied channel, and analyzer. in general, sentiment words directly reflect the surface sentiment, while the text without sentiment words reflects the implied sentiment. hence, we split the sentiment words of input text to literal channel, and the remaining words to implied channel by decomposer. then we use the literal channel to model surface meaning, and the implied channel to model hidden meaning. lastly, we use analyzer to recognize the conflict. experiments on three benchmark datasets (i.e.,, iac-v1, iac-v2 and tweets) show that our proposed dc-net model achieves state-of-the-art performance. the main contributions of this paper are twofold. first, to the best of our knowledge, the dualchannel framework is the first attempt to explicitly separate literal meaning and implied meaning to recognize sarcasm by detecting sentiment conflict. second, experiments conducted on bench-mark datasets (i.e., iac-v1/v2 and tweets) show that dc-net achieves state-of-the-art performance. ## related work prior methods of sarcasm recognition can be divided into traditional models and neural models. there are also methods considering context information, e.g., posting history @xcite , and user profile @xcite . however, such context may not be always available. ## dual-channel network (dc-net) the architecture of the proposed dc-net is shown in figure 2 . it consists of four modules: decomposer, literal channel, implied channel, and analyzer. given an input text, we use the decomposer to split it into two sub-sentences corresponding to the two channels. then we use these two channels to derive literal and implied representations independently. lastly, the analyzer predicts whether the text is sarcastic or not by detecting sentiment conflict. ## conclusion in this study, we argue that the essential characteristic of sarcastic text is the conflict between literal and implied sentiments in the same sentence. to this end, we propose a dual-channel framework to recognize sarcasm by decomposing the input text into the literal channel and the implied channel. based on this framework, we develop dc-net. dc-net is capable of exploiting the literal sentiment by encoding the sentiment words of input text, and exploiting the implied sentiment by encoding the remaining text. experiments show that the proposed dc-net achieves state-of-the-art performance.