Understanding Jargon: Combining Extraction and Generation for Definition Modeling
Jie Huang, Hanyin Shao, Kevin Chen-Chuan Chang, Jinjun Xiong, Wen-mei Hwu
Abstract
Can machines know what twin prime is? From the composition of this phrase, machines may guess twin prime is a certain kind of prime, but it is still difficult to deduce exactly what twin stands for without additional knowledge. Here, twin prime is a jargon - a specialized term used by experts in a particular field. Explaining jargon is challenging since it usually requires domain knowledge to understand. Recently, there is an increasing interest in extracting and generating definitions of words automatically. However, existing approaches, either extraction or generation, perform poorly on jargon. In this paper, we propose to combine extraction and generation for jargon definition modeling: first extract self- and correlative definitional information of target jargon from the Web and then generate the final definitions by incorporating the extracted definitional information. Our framework is remarkably simple but effective: experiments demonstrate our method can generate high-quality definitions for jargon and outperform state-of-the-art models significantly, e.g., BLEU score from 8.76 to 22.66 and human-annotated score from 2.34 to 4.04.- Anthology ID:
- 2022.emnlp-main.266
- Volume:
- Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates
- Editors:
- Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3994–4004
- Language:
- URL:
- https://aclanthology.org/2022.emnlp-main.266
- DOI:
- 10.18653/v1/2022.emnlp-main.266
- Cite (ACL):
- Jie Huang, Hanyin Shao, Kevin Chen-Chuan Chang, Jinjun Xiong, and Wen-mei Hwu. 2022. Understanding Jargon: Combining Extraction and Generation for Definition Modeling. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 3994–4004, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
- Cite (Informal):
- Understanding Jargon: Combining Extraction and Generation for Definition Modeling (Huang et al., EMNLP 2022)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2022.emnlp-main.266.pdf