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        <title>Unified Humor Detection Based on Sentence-pair Augmentation and Transfer Learning</title>
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    <name type="personal">
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    <name type="personal">
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    <name type="personal">
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    <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.</abstract>
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%0 Conference Proceedings
%T Unified Humor Detection Based on Sentence-pair Augmentation and Transfer Learning
%A Wang, Minghan
%A Yang, Hao
%A Qin, Ying
%A Sun, Shiliang
%A Deng, Yao
%S Proceedings of the 22nd Annual Conference of the European Association for Machine Translation
%D 2020
%8 nov
%I European Association for Machine Translation
%C Lisboa, Portugal
%F wang-etal-2020-unified
%X 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.
%U https://aclanthology.org/2020.eamt-1.7
%P 53-59

Markdown (Informal)

[Unified Humor Detection Based on Sentence-pair Augmentation and Transfer Learning](https://aclanthology.org/2020.eamt-1.7) (Wang et al., EAMT 2020)

• Unified Humor Detection Based on Sentence-pair Augmentation and Transfer Learning (Wang et al., EAMT 2020)

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.