A Comparison of Algorithms for Detection of “Figurativeness” in Metaphor, Irony and Puns

Mikhalkova, Elena; Ganzherli, Nadezhda; Maraev, Vladislav; Glazkova, Anna; Grigoriev, Dmitriy

doi:10.1007/978-3-030-37334-4_17

Cited by 2 publications

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References 17 publications

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“…In the NLP universe, there's been substantial recent interest in automated detection of metaphor (Dankers et al, 2019;Mikhalkova et al, 2019;Mao et al, 2019;Igamberdiev and Shin, 2018;Marhula et al, 2019;Markert, 2019;Saund et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Go Figure! Multi-task transformer-based architecture for metaphor detection using idioms: ETS team in 2020 metaphor shared task

Chen¹,

Leong²,

Flor³

et al. 2020

Proceedings of the Second Workshop on Figurative Language Processing

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This paper describes the ETS entry to the 2020 Metaphor Detection shared task. Our contribution consists of a sequence of experiments using BERT, starting with a baseline, strengthening it by spell-correcting the TOEFL corpus, followed by a multi-task learning setting, where one of the tasks is the token-level metaphor classification as per the shared task, while the other is meant to provide additional training that we hypothesized to be relevant to the main task. In one case, out-of-domain data manually annotated for metaphor is used for the auxiliary task; in the other case, indomain data automatically annotated for idioms is used for the auxiliary task. Both multitask experiments yield promising results.

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Section: Introductionmentioning

confidence: 99%

Go Figure! Multi-task transformer-based architecture for metaphor detection using idioms: ETS team in 2020 metaphor shared task

Chen¹,

Leong²,

Flor³

et al. 2020

Proceedings of the Second Workshop on Figurative Language Processing

View full text Add to dashboard Cite

show abstract

“…In the NLP universe, there's been substantial recent interest in automated detection of metaphor Mikhalkova et al, 2019;Igamberdiev and Shin, 2018;Marhula et al, 2019;Markert, 2019;Saund et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Proceedings of the Second Workshop on Figurative Language Processing

2020

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Detecting sarcasm and verbal irony is critical for understanding people's actual sentiments and beliefs. Thus, the field of sarcasm analysis has become a popular research problem in natural language processing. As the community working on computational approaches for sarcasm detection is growing, it is imperative to conduct benchmarking studies to analyze the current state-of-the-art, facilitating progress in this area. We report on the shared task on sarcasm detection we conducted as a part of the 2nd Workshop on Figurative Language Processing (FigLang 2020) at ACL 2020. Referenceskalaivani A and Thenmozhi D. 2020. Sarcasm identification and detection in conversion context using BERT. In Proceedings of the Second Workshop on Figurative Language Processing,

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A Comparison of Algorithms for Detection of “Figurativeness” in Metaphor, Irony and Puns

Cited by 2 publications

References 17 publications

Go Figure! Multi-task transformer-based architecture for metaphor detection using idioms: ETS team in 2020 metaphor shared task

Go Figure! Multi-task transformer-based architecture for metaphor detection using idioms: ETS team in 2020 metaphor shared task

Proceedings of the Second Workshop on Figurative Language Processing

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