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Rule-based Morphological Inflection Improves Neural Terminology Translation ...
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Improving Multilingual Neural Machine Translation with Auxiliary Source Languages ...
Abstract: 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. ...
URL: https://dx.doi.org/10.48448/12pz-vv60
https://underline.io/lecture/38419-improving-multilingual-neural-machine-translation-with-auxiliary-source-languages
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