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

arXiv: https://arxiv.org/abs/2202.09694 · HTML: https://mlsanity.com/paper/2202.09694/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 27 references that are in the map.
- First posted on arXiv 19 Feb 2022.

## Builds on (most cited first, 5 of 5)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [What Does This Acronym Mean? Introducing a New Dataset for Acronym Identification and Disambiguation](https://mlsanity.com/paper/2010.14678/) (2020)
- [MadDog: A Web-based System for Acronym Identification and Disambiguation](https://mlsanity.com/paper/2101.09893/) (2021)
- [AT-BERT: Adversarial Training BERT for Acronym Identification Winning Solution for SDU@AAAI-21](https://mlsanity.com/paper/2101.03700/) (2021)

## Built on by (most cited first, 4 of 4)

- [GLADIS: A General and Large Acronym Disambiguation Benchmark](https://mlsanity.com/paper/2302.01860/) (2023)
- [PLOD: An Abbreviation Detection Dataset for Scientific Documents](https://mlsanity.com/paper/2204.12061/) (2022)
- [Domain Adaptive Pretraining for Multilingual Acronym Extraction](https://mlsanity.com/paper/2206.15221/) (2022)
- [OpenMSD: Towards Multilingual Scientific Documents Similarity Measurement](https://mlsanity.com/paper/2309.10539/) (2023)

## Abstract (arXiv)

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 biomedical). As such, challenges of AE in other languages and domains is mainly unexplored. Lacking annotated datasets in multiple languages and domains has been a major issue to hinder research in this area. To address this limitation, we propose a new dataset for multilingual multi-domain AE. Specifically, 27,200 sentences in 6 typologically different languages and 2 domains, i.e., Legal and Scientific, is manually annotated for AE. Our extensive experiments on the proposed dataset show that AE in different languages and different learning settings has unique challenges, emphasizing the necessity of further research on multilingual and multi-domain AE.