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://aclanthology.org/2020.eamt-1.7
- DOI:
- 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)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2020.eamt-1.7.pdf