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
Venue:
EAMT
SIG:
Publisher:
European Association for Machine Translation
Note:
Pages:
53–59
Language:
URL:
https://www.aclweb.org/anthology/2020.eamt-1.7
DOI:
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http://aclanthology.lst.uni-saarland.de/2020.eamt-1.7.pdf