Understanding Jargon: Combining Extraction and Generation for Definition Modeling

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 3994 - 4004 December 7-11, 2022 ©2022 Association for Computational Linguistics Understanding Jargon: Combining Extraction and Generation for Definition Modeling Jie Huang1 Hanyin Shao1 Kevin Chen-Chuan Chang1 Jinjun Xiong2 Wen-mei Hwu1,3 1University of Illinois at Urbana-Champaign, USA 2University at Buffalo, USA 3NVIDIA, USA {jeffhj, hanyins2, kcchang, w-hwu}@illinois.edu jinjun@buffalo.edu 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 extrac- tion 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 in- formation of target jargon from the Web and then generate the final definitions by incorpo- rating the extracted definitional information. Our framework is remarkably simple but ef- fective: experiments demonstrate our method can generate high-quality definitions for jargon and outperform state-of-the-art models signif- icantly, e.g., BLEU score from 8.76 to 22.66 and human-annotated score from 2.34 to 4.04.1 1 Introduction Jargons are specialized terms associated with a par- ticular discipline or field. To understand jargons, a straightforward approach is to read their defini- tions, which are highly summarized sentences that capture the main characteristics of them. For in- stance, given jargon twin prime, people can know its meaning by reading its definition: “A twin prime is a prime number that is either 2 less or 2 more than another prime number.” Recently, acquiring definitions of words/phrases automatically has aroused increasing interest. There are two main approaches: extractive, corre- sponding to definition extraction, where definitions 1Code and data are available at https://github.com/ jeffhj/CDM. are extracted from existing corpora automatically (Anke and Schockaert, 2018; Veyseh et al., 2020; Kang et al., 2020); and abstractive, corresponding to definition generation, where definitions are gen- erated conditioned with the target words/phrases and the contexts in which they are used (Noraset et al., 2017; Gadetsky et al., 2018; Bevilacqua et al., 2020; August et al., 2022; Gardner et al., 2022). In this paper, we study jargon definition mod- eling, which aims to acquire definitions for jargon automatically. Jargon definition modeling is impor- tant since definitions of jargon are less likely to be organized in an existing dictionary/encyclopedia and such terms are difficult for non-experts to understand without explanations (Bullock et al., 2019). This is particularly true for new jargon from fast-advancing fields. For instance, neither Oxford dictionary (Butterfield et al., 2016) nor Wikipedia2 includes few-shot learning– an important setup in machine learning. However, to acquire definitions for jargon, both extractive and abstractive approaches may fail. Ex- tracting high-quality definitions would …

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