Improving Multilingual Neural Machine Translation with Auxiliary Source Languages
Multilingual neural machine translation models typically handle one source language at a time. However, prior work has shown that translating from multiple source languages improves translation quality. Different from existing approaches on multi-source translation that are limited to the test scenario where parallel source sentences from multiple languages are available at inference time, we propose to improve multilingual translation in a more common scenario by exploiting synthetic source sentences from auxiliary languages. We train our model on synthetic multi-source corpora and apply random masking to enable flexible inference with single-source or bi-source inputs. Extensive experiments on Chinese/English-Japanese and a large-scale multilingual translation benchmark show that our model outperforms the multilingual baseline significantly by up to +4.0 BLEU with the largest improvements on low-resource or distant language pairs.
Code (1)
Tasks
Machine TranslationTranslationSimilar Papers 제목 키워드 기반
Multilingual word translation using auxiliary languages
Current multilingual word translation methods are focused on jointly learning mappings from each language to a shared space. The actual translation, however, is still performed as an isolated bilingual task. In this stud…
TranslationWord TranslationAnalyzing and Improving Cross-lingual Knowledge Transfer for Machine Translation
Multilingual machine translation systems aim to make knowledge accessible across languages, yet learning effective cross-lingual representations remains challenging. These challenges are especially pronounced for low-res…
Machine TranslationMultilingual Agreement for Multilingual Neural Machine Translation
Although multilingual neural machine translation (MNMT) enables multiple language translations, the training process is based on independent multilingual objectives. Most multilingual models can not explicitly exploit di…
Machine TranslationTranslationTarget Conditioned Sampling: Optimizing Data Selection for Multilingual Neural Machine Translation
To improve low-resource Neural Machine Translation (NMT) with multilingual corpora, training on the most related high-resource language only is often more effective than using all data available (Neubig and Hu, 2018). Ho…
Low Resource Neural Machine TranslationLow-Resource Neural Machine TranslationLow Resource NMTMachine Translation+3Auxiliary Subword Segmentations as Related Languages for Low Resource Multilingual Translation
We propose a novel technique that combines alternative subword tokenizations of a single source-target language pair that allows us to leverage multilingual neural translation training methods. These alternate segmentati…
Cross-Lingual TransferTranslation