Improving Lexically Constrained Neural Machine Translation with Source-Conditioned Masked Span Prediction

• URL: http://arxiv.org/abs/2105.05498v1
• Date: Wed, 12 May 2021 08:11:33 GMT
• Title: Improving Lexically Constrained Neural Machine Translation with Source-Conditioned Masked Span Prediction
• Authors: Gyubok Lee, Seongjun Yang, Edward Choi
• Abstract summary: In this paper, we tackle a more challenging setup consisting of domain-specific corpora with much longer n-gram and highly specialized terms. To encourage span-level representations in generation, we additionally impose a source-sentence conditioned masked span prediction loss in the decoder. Experimental results on three domain-specific corpora in two language pairs demonstrate that the proposed training scheme can improve the performance of existing lexically constrained methods.
• Score: 6.46964825569749
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generating accurate terminology is a crucial component for the practicality and reliability of neural machine translation (NMT) systems. To address this, lexically constrained NMT explores various methods to ensure pre-specified words and phrases to appear in the translations. In many cases, however, those methods are evaluated on general domain corpora, where the terms are mostly uni- and bi-grams (>98%). In this paper, we instead tackle a more challenging setup consisting of domain-specific corpora with much longer n-gram and highly specialized terms. To encourage span-level representations in generation, we additionally impose a source-sentence conditioned masked span prediction loss in the decoder and observe improvements on both terminology translation as well as BLEU scores. Experimental results on three domain-specific corpora in two language pairs demonstrate that the proposed training scheme can improve the performance of existing lexically constrained methods that can operate both with or without a term dictionary at test time.

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