Machine translationese: Effects of algorithmic bias on linguistic complexity in machine translation
Vanmassenhove,Eva Shterionov,Dimitar Gwilliam,Matthew
Vanmassenhove,Eva
Shterionov,Dimitar
Gwilliam,Matthew
Abstract
Recent studies in the field of Machine Translation (MT) and Natural Language Processing (NLP) have shown that existing models amplify biases observed in the training data. The amplification of biases in language technology has mainly been examined with respect to specific phenomena, such as gender bias. In this work, we go beyond the study of gender in MT and investigate how bias amplification might affect language in a broader sense. We hypothesize that the 'algorithmic bias', i.e. an exacerbation of frequently observed patterns in combination with a loss of less frequent ones, not only exacerbates societal biases present in current datasets but could also lead to an artificially impoverished language: 'machine translationese'. We assess the linguistic richness (on a lexical and morphological level) of translations created by different data-driven MT paradigms - phrase-based statistical (PB-SMT) and neural MT (NMT). Our experiments show that there is a loss of lexical and morphological richness in the translations produced by all investigated MT paradigms for two language pairs (EN↔FR and EN↔ES).
Description
Publisher Copyright: © 2021 Association for Computational Linguistics
Date
2021-05
Journal Title
Journal ISSN
Volume Title
Publisher
Association for Computational Linguistics (ACL)
Research Projects
Organizational Units
Journal Issue
Keywords
SDG 5 - Gender Equality
Citation
Vanmassenhove, E, Shterionov, D & Gwilliam, M 2021, Machine translationese : Effects of algorithmic bias on linguistic complexity in machine translation. in EACL 2021 - 16th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference. EACL 2021 - 16th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference, Association for Computational Linguistics (ACL), pp. 2203-2213, 16th Conference of the European Chapter of the Associationfor Computational Linguistics, EACL 2021, Virtual, Online, 19/04/21. https://doi.org/10.18653/v1/2021.eacl-main.188
License
info:eu-repo/semantics/openAccess