Automated Sentence Alignment in Ukrainian-German Parallel Texts

Abstract

Introduction. Sentence alignment in German parallel texts is a relevant task. It allows obtaining parallel data sets necessary for many computational linguistics tasks, such as parallel corpus construction and machine translation. The article describes the main tasks of sentence alignment, reviews existing methods and analyzes their ideas. Based on this analysis, a new method is proposed, it is based on the Bleualign approach, which uses machine translation systems and the BLEU metric to assess the similarity of sentences. However, it differs in the use of additional marker dictionaries for industry terms and conjunctions, including their synonyms. This article outlines the main tasks of sentence alignment, reviews existing methods, and discusses their ideas. Based on this analysis, a new method is proposed. This method is based on the Bleualign approach, which uses machine translation systems and BLEU metrics to evaluate sentence similarity, including the alignment of parts of complex sentences. However, it differs in the alignment process steps and introduces additional marker dictionaries for domain-specific words and conjunctions, including their synonyms.

The purpose of the work is to develop a method and software for automated sentence alignment in Ukrainian-German parallel texts.

Methods. The developed method is based on the Bleualign method and the BLEU metric. It is improved by the use of dictionaries of industry terms and conjunctions, and also provides a focus on one language pair — Ukrainian-German. The proposed method consists of 6 stages, allowing to align sentences in Ukrainian-German parallel texts. The proposed method is implemented in software using the Python programming language.

Results. A new method for aligning sentences for Ukrainian-German parallel texts has been developed and its software implementation has been completed. The proposed method is based on statistical approaches and does not require significant computing resources. Unlike the Bleualign method, it uses dictionaries of industry terms and conjunctions for more accurate sentence alignment.

Conclusions. Further research will include experiments and comparison of the alignment results obtained using the proposed method with the results of the Bleualign method.