Domain Adaptation in Multilingual and Multi-Domain Monolingual Settings for Complex Word Identification

George-Eduard Zaharia, Răzvan-Alexandru Smădu, Dumitru Cercel, Mihai Dascalu


Abstract
Complex word identification (CWI) is a cornerstone process towards proper text simplification. CWI is highly dependent on context, whereas its difficulty is augmented by the scarcity of available datasets which vary greatly in terms of domains and languages. As such, it becomes increasingly more difficult to develop a robust model that generalizes across a wide array of input examples. In this paper, we propose a novel training technique for the CWI task based on domain adaptation to improve the target character and context representations. This technique addresses the problem of working with multiple domains, inasmuch as it creates a way of smoothing the differences between the explored datasets. Moreover, we also propose a similar auxiliary task, namely text simplification, that can be used to complement lexical complexity prediction. Our model obtains a boost of up to 2.42% in terms of Pearson Correlation Coefficients in contrast to vanilla training techniques, when considering the CompLex from the Lexical Complexity Prediction 2021 dataset. At the same time, we obtain an increase of 3% in Pearson scores, while considering a cross-lingual setup relying on the Complex Word Identification 2018 dataset. In addition, our model yields state-of-the-art results in terms of Mean Absolute Error.
Anthology ID:
2022.acl-long.6
Volume:
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
70–80
Language:
URL:
https://aclanthology.org/2022.acl-long.6
DOI:
10.18653/v1/2022.acl-long.6
Bibkey:
Cite (ACL):
George-Eduard Zaharia, Răzvan-Alexandru Smădu, Dumitru Cercel, and Mihai Dascalu. 2022. Domain Adaptation in Multilingual and Multi-Domain Monolingual Settings for Complex Word Identification. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 70–80, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
Domain Adaptation in Multilingual and Multi-Domain Monolingual Settings for Complex Word Identification (Zaharia et al., ACL 2022)
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PDF:
https://aclanthology.org/2022.acl-long.6.pdf