MACRONYM: A Large-Scale Dataset for Multilingual and Multi-Domain Acronym Extraction

Proceedings of the 29th International Conference on Computational Linguistics, pages 3309–3314 October 12–17, 2022. 3309 MACRONYM: A Large-Scale Dataset for Multilingual and Multi-Domain Acronym Extraction Amir Pouran Ben Veyseh1, Nicole Meister2, Seunghyun Yoon3, Rajiv Jain3, Franck Dernoncourt3, and Thien Huu Nguyen1 1Department of Computer Science, University of Oregon, OR, USA 2Department of Computer Science, Stanford University, CA, USA 3Adobe Research, USA {apouranb, thien}@cs.uoregon.edu, nmeist@stanford.edu {syoon,rajijain,franck.dernoncourt}@adobe.com Abstract Acronym extraction is the task of identifying acronyms and their expanded forms in texts that is necessary for various NLP applications. Despite major progress for this task in recent years, one limitation of existing AE research is that they are limited to the English language and certain domains (i.e., scientific and biomed- ical). Challenges of AE in other languages and domains are mainly unexplored. As such, lack- ing annotated datasets in multiple languages and domains has been a major issue to pre- vent research in this direction. To address this limitation, we propose a new dataset for mul- tilingual and multi-domain AE. Specifically, 27,200 sentences in 6 different languages and 2 new domains, i.e., legal and scientific, are man- ually annotated for AE. Our experiments on the dataset show that AE in different languages and learning settings has unique challenges, emphasizing the necessity of further research on multilingual and multi-domain AE. 1 Introduction Acronyms are short forms of longer phrases that are often constructed using a few letters selected from the long phrases. Due to their functionality, acronyms are common in many languages and do- mains. For instance, 73% of abstracts of scientific papers contain at least an acronym (Barnett and Doubleday, 2020). As such, in text processing ap- plications, e.g., question answering and machine translation, it is necessary to correctly identify the acronyms and their meanings. Toward this goal, our work focus on the task of Acronym Extraction (AE), aiming to recognize acronyms and their def- initions/long forms in text. For instance, in the sentence “They will meet in the conference of the World Trade Organization (WTO)", an AE system should identify “WTO” and “World Trade Organi- zation” as the acronym and long form, respectively. Despite all progress in recent years, prior work on AE has mainly limited to specific domains and languages. Specifically, biomedical and scientific texts in English have been the main focus in prior work. However, recognition of acronyms in other languages and domains is also important and might involve challenges not reflected in English biomed- ical/scientific texts. For instance, many existing AE methods for English employ uppercase letters to identify acronyms (Veyseh et al., 2020). How- ever, in non-case sensitive languages, e.g., Arabic or Persian, uppercase letter concept does not ex- ist, thus causing a failure of existing AE systems. Moreover, in each domain or language, different styles might be exerted to shorten a longer phrase to produce acronyms. For instance, initial letters of the words in the phrases are commonly used to form acronyms in scientific English; however, in le- gal English or Danish documents, the use of …

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