@inproceedings{ilic-etal-2018-deep,
title = "Deep contextualized word representations for detecting sarcasm and irony",
author = "Ili{\'c}, Suzana and
Marrese-Taylor, Edison and
Balazs, Jorge and
Matsuo, Yutaka",
editor = "Balahur, Alexandra and
Mohammad, Saif M. and
Hoste, Veronique and
Klinger, Roman",
booktitle = "Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
month = oct,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W18-6202",
doi = "10.18653/v1/W18-6202",
pages = "2--7",
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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<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.</abstract>
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%0 Conference Proceedings
%T Deep contextualized word representations for detecting sarcasm and irony
%A Ilić, Suzana
%A Marrese-Taylor, Edison
%A Balazs, Jorge
%A Matsuo, Yutaka
%Y Balahur, Alexandra
%Y Mohammad, Saif M.
%Y Hoste, Veronique
%Y Klinger, Roman
%S Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
%D 2018
%8 October
%I Association for Computational Linguistics
%C Brussels, Belgium
%F ilic-etal-2018-deep
%X 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.
%R 10.18653/v1/W18-6202
%U https://aclanthology.org/W18-6202
%U https://doi.org/10.18653/v1/W18-6202
%P 2-7
Markdown (Informal)
[Deep contextualized word representations for detecting sarcasm and irony](https://aclanthology.org/W18-6202) (Ilić et al., WASSA 2018)
ACL