讽刺
计算机科学
话语
自然语言处理
人工智能
判决
对话框
语音识别
语言学
讽刺
哲学
万维网
作者
Manjot Bedi,Shivani Kumar,Md Shad Akhtar,Tanmoy Chakraborty
出处
期刊:IEEE Transactions on Affective Computing
[Institute of Electrical and Electronics Engineers]
日期:2023-04-01
卷期号:14 (2): 1363-1375
被引量:12
标识
DOI:10.1109/taffc.2021.3083522
摘要
Sarcasm detection and humor classification are inherently subtle problems, primarily due to their dependence on the contextual and non-verbal information. Furthermore, existing studies in these two topics are usually constrained in non-English languages such as Hindi, due to the unavailability of qualitative annotated datasets. In this work, we make two major contributions considering the above limitations: (1) we develop a Hindi-English code-mixed dataset, MaSaC , 1 for the multi-modal sarcasm detection and humor classification in conversational dialog, which to our knowledge is the first dataset of its kind; (2) we propose MSH-COMICS , 2 a novel attention-rich neural architecture for the utterance classification. We learn efficient utterance representation utilizing a hierarchical attention mechanism that attends to a small portion of the input sentence at a time. Further, we incorporate dialog-level contextual attention mechanism to leverage the dialog history for the multi-modal classification. We perform extensive experiments for both the tasks by varying multi-modal inputs and various submodules of MSH-COMICS . We also conduct comparative analysis against existing approaches. We observe that MSH-COMICS attains superior performance over the existing models by $>$ 1 F1-score point for the sarcasm detection and 10 F1-score points in humor classification. We diagnose our model and perform thorough analysis of the results to understand the superiority and pitfalls.
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