Year: 2017
Type: conference-paper
Abstract: Although new corpora are becoming increasingly available for machine translation, only those that belong to the same or similar domains are typically able to improve translation performance.Recently Neural Machine Translation (NMT) has become prominent in the field.However, most of the existing domain adaptation methods only focus on phrase-based machine translation.In this paper, we exploit the NMT's internal embedding of the source sentence and use the sentence embedding similarity to select the sentences which are close to in-domain data.The empirical adaptation results on the IWSLT English-French and NIST Chinese-English tasks show that the proposed methods can substantially improve NMT performance by 2.4-9.0BLEU points, outperforming the existing state-of-the-art baseline by 2.3-4.5 BLEU points.
DOI: 10.18653/v1/p17-2089
Authors: Rui Wang, Andrew Finch, Masao Utiyama, Eiichiro Sumita
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W2740743644