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        <title>TEDB System Description to a Shared Task on Euphemism Detection 2022</title>
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            <namePart type="given">Debanjan</namePart>
            <namePart type="family">Ghosh</namePart>
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        <name type="personal">
            <namePart type="given">Beata</namePart>
            <namePart type="family">Beigman Klebanov</namePart>
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        <name type="personal">
            <namePart type="given">Smaranda</namePart>
            <namePart type="family">Muresan</namePart>
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        <name type="personal">
            <namePart type="given">Anna</namePart>
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        <name type="personal">
            <namePart type="given">Soujanya</namePart>
            <namePart type="family">Poria</namePart>
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        <name type="personal">
            <namePart type="given">Tuhin</namePart>
            <namePart type="family">Chakrabarty</namePart>
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    <abstract>In this report, we describe our Transformers for euphemism detection baseline (TEDB) submissions to a shared task on euphemism detection 2022. We cast the task of predicting euphemism as text classification. We considered Transformer-based models which are the current state-of-the-art methods for text classification. We explored different training schemes, pretrained models, and model architectures. Our best result of 0.816 F1-score (0.818 precision and 0.814 recall) consists of a euphemism-detection-finetuned TweetEval/TimeLMs-pretrained RoBERTa model as a feature extractor frontend with a KimCNN classifier backend trained end-to-end using a cosine annealing scheduler. We observed pretrained models on sentiment analysis and offensiveness detection to correlate with more F1-score while pretraining on other tasks, such as sarcasm detection, produces less F1-scores. Also, putting more word vector channels does not improve the performance in our experiments.</abstract>
    <identifier type="citekey">wiriyathammabhum-2022-tedb</identifier>
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%0 Conference Proceedings
%T TEDB System Description to a Shared Task on Euphemism Detection 2022
%A Wiriyathammabhum, Peratham
%Y Ghosh, Debanjan
%Y Beigman Klebanov, Beata
%Y Muresan, Smaranda
%Y Feldman, Anna
%Y Poria, Soujanya
%Y Chakrabarty, Tuhin
%S Proceedings of the 3rd Workshop on Figurative Language Processing (FLP)
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates (Hybrid)
%F wiriyathammabhum-2022-tedb
%X In this report, we describe our Transformers for euphemism detection baseline (TEDB) submissions to a shared task on euphemism detection 2022. We cast the task of predicting euphemism as text classification. We considered Transformer-based models which are the current state-of-the-art methods for text classification. We explored different training schemes, pretrained models, and model architectures. Our best result of 0.816 F1-score (0.818 precision and 0.814 recall) consists of a euphemism-detection-finetuned TweetEval/TimeLMs-pretrained RoBERTa model as a feature extractor frontend with a KimCNN classifier backend trained end-to-end using a cosine annealing scheduler. We observed pretrained models on sentiment analysis and offensiveness detection to correlate with more F1-score while pretraining on other tasks, such as sarcasm detection, produces less F1-scores. Also, putting more word vector channels does not improve the performance in our experiments.
%R 10.18653/v1/2022.flp-1.1
%U https://aclanthology.org/2022.flp-1.1
%U https://doi.org/10.18653/v1/2022.flp-1.1
%P 1-7

Markdown (Informal)

[TEDB System Description to a Shared Task on Euphemism Detection 2022](https://aclanthology.org/2022.flp-1.1) (Wiriyathammabhum, Fig-Lang 2022)

• TEDB System Description to a Shared Task on Euphemism Detection 2022 (Wiriyathammabhum, Fig-Lang 2022)

ACL

• Peratham Wiriyathammabhum. 2022. TEDB System Description to a Shared Task on Euphemism Detection 2022. In Proceedings of the 3rd Workshop on Figurative Language Processing (FLP), pages 1–7, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.