Unified Humor Detection Based on Sentence-pair Augmentation and Transfer Learning

Minghan Wang, Hao Yang, Ying Qin, Shiliang Sun, Yao Deng


Abstract
We propose a unified multilingual model for humor detection which can be trained under a transfer learning framework. 1) The model is built based on pre-trained multilingual BERT, thereby is able to make predictions on Chinese, Russian and Spanish corpora. 2) We step out from single sentence classification and propose sequence-pair prediction which considers the inter-sentence relationship. 3) We propose the Sentence Discrepancy Prediction (SDP) loss, aiming to measure the semantic discrepancy of the sequence-pair, which often appears in the setup and punchline of a joke. Our method achieves two SoTA and a second-place on three humor detection corpora in three languages (Russian, Spanish and Chinese), and also improves F1-score by 4%-6%, which demonstrates the effectiveness of it in humor detection tasks.
Anthology ID:
2020.eamt-1.7
Volume:
Proceedings of the 22nd Annual Conference of the European Association for Machine Translation
Month:
November
Year:
2020
Address:
Lisboa, Portugal
Editors:
André Martins, Helena Moniz, Sara Fumega, Bruno Martins, Fernando Batista, Luisa Coheur, Carla Parra, Isabel Trancoso, Marco Turchi, Arianna Bisazza, Joss Moorkens, Ana Guerberof, Mary Nurminen, Lena Marg, Mikel L. Forcada
Venue:
EAMT
SIG:
Publisher:
European Association for Machine Translation
Note:
Pages:
53–59
Language:
URL:
https://aclanthology.org/2020.eamt-1.7
DOI:
Bibkey:
Cite (ACL):
Minghan Wang, Hao Yang, Ying Qin, Shiliang Sun, and Yao Deng. 2020. Unified Humor Detection Based on Sentence-pair Augmentation and Transfer Learning. In Proceedings of the 22nd Annual Conference of the European Association for Machine Translation, pages 53–59, Lisboa, Portugal. European Association for Machine Translation.
Cite (Informal):
Unified Humor Detection Based on Sentence-pair Augmentation and Transfer Learning (Wang et al., EAMT 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.eamt-1.7.pdf