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        <title>Large Dataset and Language Model Fun-Tuning for Humor Recognition</title>
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    <name type="personal">
        <namePart type="given">Vladislav</namePart>
        <namePart type="family">Blinov</namePart>
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    <name type="personal">
        <namePart type="given">Valeria</namePart>
        <namePart type="family">Bolotova-Baranova</namePart>
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    <name type="personal">
        <namePart type="given">Pavel</namePart>
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            <title>Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics</title>
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            <namePart type="given">Anna</namePart>
            <namePart type="family">Korhonen</namePart>
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        <name type="personal">
            <namePart type="given">David</namePart>
            <namePart type="family">Traum</namePart>
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    <abstract>The task of humor recognition has attracted a lot of attention recently due to the urge to process large amounts of user-generated texts and rise of conversational agents. We collected a dataset of jokes and funny dialogues in Russian from various online resources and complemented them carefully with unfunny texts with similar lexical properties. The dataset comprises of more than 300,000 short texts, which is significantly larger than any previous humor-related corpus. Manual annotation of 2,000 items proved the reliability of the corpus construction approach. Further, we applied language model fine-tuning for text classification and obtained an F1 score of 0.91 on a test set, which constitutes a considerable gain over baseline methods. The dataset is freely available for research community.</abstract>
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%0 Conference Proceedings
%T Large Dataset and Language Model Fun-Tuning for Humor Recognition
%A Blinov, Vladislav
%A Bolotova-Baranova, Valeria
%A Braslavski, Pavel
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F blinov-etal-2019-large
%X The task of humor recognition has attracted a lot of attention recently due to the urge to process large amounts of user-generated texts and rise of conversational agents. We collected a dataset of jokes and funny dialogues in Russian from various online resources and complemented them carefully with unfunny texts with similar lexical properties. The dataset comprises of more than 300,000 short texts, which is significantly larger than any previous humor-related corpus. Manual annotation of 2,000 items proved the reliability of the corpus construction approach. Further, we applied language model fine-tuning for text classification and obtained an F1 score of 0.91 on a test set, which constitutes a considerable gain over baseline methods. The dataset is freely available for research community.
%R 10.18653/v1/P19-1394
%U https://aclanthology.org/P19-1394
%U https://doi.org/10.18653/v1/P19-1394
%P 4027-4032

Markdown (Informal)

[Large Dataset and Language Model Fun-Tuning for Humor Recognition](https://aclanthology.org/P19-1394) (Blinov et al., ACL 2019)

• Large Dataset and Language Model Fun-Tuning for Humor Recognition (Blinov et al., ACL 2019)

ACL

• Vladislav Blinov, Valeria Bolotova-Baranova, and Pavel Braslavski. 2019. Large Dataset and Language Model Fun-Tuning for Humor Recognition. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 4027–4032, Florence, Italy. Association for Computational Linguistics.