Large Dataset and Language Model Fun-Tuning for Humor Recognition

Vladislav Blinov, Valeria Bolotova-Baranova, Pavel Braslavski


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.
Anthology ID:
P19-1394
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4027–4032
Language:
URL:
https://aclanthology.org/P19-1394
DOI:
10.18653/v1/P19-1394
Bibkey:
Cite (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.
Cite (Informal):
Large Dataset and Language Model Fun-Tuning for Humor Recognition (Blinov et al., ACL 2019)
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PDF:
https://aclanthology.org/P19-1394.pdf