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arXiv · 1809.09795

Deep contextualized word representations for detecting sarcasm and irony

Suzana Ilić·Edison Marrese-Taylor·Jorge A. Balazs·Yutaka Matsuo
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Abstract

Predicting context-dependent and non-literal utterances like sarcastic and ironic expressions still remains a challenging task in NLP, as it goes beyond linguistic patterns, encompassing common sense and shared knowledge as crucial components. To capture complex morpho-syntactic features that can usually serve as indicators for irony or sarcasm across dynamic contexts, we propose a model that uses character-level vector representations of words, based on ELMo. We test our model on 7 different datasets derived from 3 different data sources, providing state-of-the-art performance in 6 of them, and otherwise offering competitive results.

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Suzana Ilić, Edison Marrese-Taylor, Jorge A. Balazs, Yutaka Matsuo. 2018-09-26. Deep contextualized word representations for detecting sarcasm and irony. https://arxiv.org/abs/1809.09795

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