“I Know Who You Are”: Character-Based Features for Conversational Humor Recognition in Chinese
Wenbo Shang, Jiangjiang Zhao, Zezhong Wang, Binyang Li, Fangchun Yang, Kam-Fai Wong
Abstract
Humor plays an important role in our daily life, as it is an essential and fascinating element in the communication between persons. Therefore, how to recognize punchlines from the dialogue, i.e. conversational humor recognition, has attracted much interest of computational linguistics communities. However, most existing work attempted to understand the conversational humor by analyzing the contextual information of the dialogue, but neglected the character of the interlocutor, such as age, gender, occupation, and so on. For instance, the same utterance could bring out humorous from a serious person, but may be a plain expression from a naive person. To this end, this paper proposes a Character Fusion Conversational Humor Recognition model (CFCHR) to explore character information to recognize conversational humor. CFCHR utilizes a multi-task learning framework that unifies two highly pertinent tasks, i.e., character extraction and punchline identification. Based on deep neural networks, we trained both tasks jointly by sharing weight to extract the common and task-invariant features while each task could still learn its task-specific features. Experiments were conducted on Chinese sitcoms corpus, which consisted of 12,677 utterances from 22 characters. The experimental results demonstrated that CFCHR could achieve 33.08% improvements in terms of F1-score over some strong baselines, and proved the effectiveness of the character information to identify the punchlines.- Anthology ID:
- 2022.findings-emnlp.212
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2022
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates
- Editors:
- Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2927–2932
- Language:
- URL:
- https://aclanthology.org/2022.findings-emnlp.212
- DOI:
- 10.18653/v1/2022.findings-emnlp.212
- Cite (ACL):
- Wenbo Shang, Jiangjiang Zhao, Zezhong Wang, Binyang Li, Fangchun Yang, and Kam-Fai Wong. 2022. “I Know Who You Are”: Character-Based Features for Conversational Humor Recognition in Chinese. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 2927–2932, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
- Cite (Informal):
- “I Know Who You Are”: Character-Based Features for Conversational Humor Recognition in Chinese (Shang et al., Findings 2022)
- PDF:
- https://preview.aclanthology.org/naacl-24-ws-corrections/2022.findings-emnlp.212.pdf