Strong baselines for complex word identification across multiple languages

Finnimore, Pierre, Fritzsch, Elisabeth, King, Daniel, Sneyd, Alison, Ur Rehman, Aneeq, Alva Manchego, Fernando and Vlachos, Andreas 2019. Strong baselines for complex word identification across multiple languages. Presented at: 2019 Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT 2019), 2-7 June 2019. Published in: Burstein, Jill, Doran, Christy and Solorio, Thamar eds. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). Association for Computational Linguistics, pp. 970-977. 10.18653/v1/N19-1102

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Abstract

Complex Word Identification (CWI) is the task of identifying which words or phrases in a sentence are difficult to understand by a target audience. The latest CWI Shared Task released data for two settings: monolingual (i.e. train and test in the same language) and cross-lingual (i.e. test in a language not seen during training). The best monolingual models relied on language-dependent features, which do not generalise in the cross-lingual setting, while the best cross-lingual model used neural networks with multi-task learning. In this paper, we present monolingual and cross-lingual CWI models that perform as well as (or better than) most models submitted to the latest CWI Shared Task. We show that carefully selected features and simple learning models can achieve state-of-the-art performance, and result in strong baselines for future development in this area. Finally, we discuss how inconsistencies in the annotation of the data can explain some of the results obtained.

Item Type:	Conference or Workshop Item (Paper)
Status:	Published
Schools:	Computer Science & Informatics
Publisher:	Association for Computational Linguistics
Last Modified:	04 Apr 2022 10:30
URI:	https://orca.cardiff.ac.uk/id/eprint/147264

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