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, 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, 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.

本研究开发了一个多模态的印地语 - 英语混合数据集用于会话中的多模态讽刺检测和幽默分类，并提出了一种新的基于注意力机制的神经架构 MSH-COMICS，该模型在两个任务中都具有优越性能。