Computational study demonstrates increased translation quality in machine translation models, suggesting that hybrid sentence- and fragment-level extraction enriches parallel training data.
Parallel texts are essential resources in linguistics, natural language processing, and multilingual information retrieval. Many studies attempt to extract parallel text from existing resources, particularly from comparable texts. The approaches to extract parallel text from comparable text can be divided into sentence-level approach and fragment-level approach. In this paper, an approach that combines sentence-level approach and fragment-level approach is proposed. The study was evaluated using statistical machine translation (SMT) and neural machine translation (NMT). The experiment results show a very significant improvement in the BLEU scores of SMT and NMT. The BLEU scores for SMT for the test in computer science domain and news domain increase from 17.45 and 41.45 to 18.56 and 48.65 respectively. On the other hand, the BLEU scores for NMT in the computer science domain and news domain increase from 14.42 and 19.39 to 21.17 and 41.75 respectively.
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Yeong et al. (2019) studied this question.
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