Deep contextualized word representations for detecting sarcasm and irony
This addresses the problem of context-dependent and non-literal utterance detection for NLP applications, representing an incremental improvement with specific gains.
The paper tackled the challenge of detecting sarcasm and irony in NLP by proposing a model using character-level vector representations based on ELMo, achieving state-of-the-art performance on 6 out of 7 datasets and competitive results on the remaining one.
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.