Abstract

This paper proposed a new subword segmentation method for neural machine translation, “Bilingual Subword Segmentation,” which tokenizes sentences to minimize the difference between the number of subword units in a sentence and that of its translation. While existing subword segmentation methods tokenize a sentence without considering its translation, the proposed method tokenizes a sentence by using subword units induced from bilingual sentences; this method could be more favorable to machine translation. Evaluations on WAT Asian Scientific Paper Excerpt Corpus (ASPEC) English-to-Japanese and Japanese-to-English translation tasks and WMT14 English-to-German and German-to-English translation tasks show that our bilingual subword segmentation improves the performance of Transformer neural machine translation (up to +0.81 BLEU).

Highlights

  • Subword units have recently been widely used in neural machine translation (NMT) to solve open vocabulary problems

  • This paper proposes a new subword segmentation method for NMT, “Bilingual Subword Segmentation,” which tokenizes sentences by using subword units induced from bilingual sentences

  • This section proposes “bilingual subword segmentation,” which tokenizes sentences by using subword units induced from bilingual sentences

Read more

Summary

Introduction

Subword units have recently been widely used in neural machine translation (NMT) to solve open vocabulary problems. Kudo (2018) has proposed a subword segmentation method based on a unigram language model, that can be applied to non-segmented languages such as Chinese and Japanese. Both BPE and the unigram language model tokenize sentences by minimizing the number of segments under a limitation on subword vocabulary size, which relies on a data compression principle. In these existing segmentations, a sentence is segmented without considering its translation, and the segmented sentence might not be optimal for NMT. For segmentation of monolingual source language sentences (i.e., test data for NMT), an LSTM-based subword segmenter for the source language is preliminarily learned from the source side of segmented bilingual sentences, and monolingual source language sentences are tokenized by the learned subword segmenter

Objectives
Results
Discussion
Conclusion

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.