Analysis of Idioms in Neural Machine Translation: A Data Set
- Authors: Skacheva N.V.1
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Affiliations:
- Issue: No 3 (2024)
- Pages: 55-63
- Section: Articles
- URL: https://journal-vniispk.ru/2454-0714/article/view/359410
- DOI: https://doi.org/10.7256/2454-0714.2024.3.71518
- EDN: https://elibrary.ru/JLJDSL
- ID: 359410
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Abstract
There has been a debate in various circles of the public for decades about whether a "machine can replace a person." This also applies to the field of translation. And so far, some are arguing, others are "making a dream come true." Therefore, now more and more research is aimed at improving machine translation systems (hereinafter MP). To understand the advantages and disadvantages of MP systems, it is necessary, first of all, to understand their algorithms. At the moment, the main open problem of neural machine translation (NMP) is the translation of idiomatic expressions. The meaning of such expressions does not consist of the meanings of their constituent words, and NMT models tend to translate them literally, which leads to confusing and meaningless translations. The research of idioms in the NMP is limited and difficult due to the lack of automatic methods. Therefore, despite the fact that modern NMP systems generate increasingly high-quality translations, the translation of idioms remains one of the unsolved tasks in this area. This is due to the fact that idioms, as a category of verbose expressions, represent an interesting linguistic phenomenon when the general meaning of an expression cannot be made up of the meanings of its parts. The first important problem is the lack of special data sets for learning and evaluating the translation of idioms. In this paper, we solve this problem by creating the first large-scale dataset for translating idioms. This data set is automatically extracted from the German translation corpus and includes a target set in which all sentences contain idioms, and a regular training corpus in which sentences containing idioms are marked. We have released this dataset and are using it to conduct preliminary experiments on NMP as a first step towards improving the translation of idioms.
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