Boosting Neural Machine Translation with Similar Translations

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 1580–1590 July 5 - 10, 2020. c⃝2020 Association for Computational Linguistics 1580 Boosting Neural Machine Translation with Similar Translations Jitao Xu†‡, Josep Crego†, Jean Senellart† †SYSTRAN, 5 rue Feydeau, 75002 Paris, France firstname.lastname@systrangroup.com ‡Universit´e Paris-Saclay, CNRS, LIMSI, 91400, Orsay, France jitao.xu@limsi.fr Abstract This paper explores data augmentation meth- ods for training Neural Machine Translation to make use of similar translations, in a com- parable way a human translator employs fuzzy matches. In particular, we show how we can simply feed the neural model with informa- tion on both source and target sides of the fuzzy matches, we also extend the similarity to include semantically related translations re- trieved using distributed sentence representa- tions. We show that translations based on fuzzy matching provide the model with “copy” information while translations based on em- bedding similarities tend to extend the trans- lation “context”. Results indicate that the ef- fect from both similar sentences are adding up to further boost accuracy, are combining nat- urally with model fine-tuning and are provid- ing dynamic adaptation for unseen translation pairs. Tests on multiple data sets and domains show consistent accuracy improvements. To foster research around these techniques, we also release an Open-Source toolkit with ef- ficient and flexible fuzzy-match implementa- tion. 1 Introduction For decades, the localization industry has been proposing Fuzzy Matching technology in CAT tools allowing the human translator to visual- ize one or several fuzzy matches from transla- tion memory when translating a sentence leading to higher productivity and consistency (Yamada, 2011). Hence, even though the concept of fuzzy match scores is not standardized and differs be- tween CAT tools (Bloodgood and Strauss, 2014), translators generally accept discounted translation rate for sentences with ”high” fuzzy matches1. With improving machine translation technology 1https://signsandsymptomsoftranslation. com/2015/03/06/fuzzy-matches/. and training of models on translation memories, machine translated output has been progressively introduced as a substitute for fuzzy matches when no sufficiently “good” fuzzy match is found and proved to also increase translator productivity given appropriate post-editing environment (Plitt and Masselot, 2010). These two technologies are entirely different in their finality - indeed, for a given source sentence, fuzzy matching is just a database retrieval and scor- ing technique always returning a pair of source and target segments, while machine translation is ac- tually building an original translation. However, with Statistical Machine Translation, the two tech- nologies are sharing the same simple idea about managing and retrieving optimal combination of longest translated n-grams and this property led to the development of several techniques like use of fuzzy matches in SMT decoding (Koehn and Senellart, 2010; Wang et al., 2013), adaptive ma- chine translation (Zaretskaya et al., 2015) or “fuzzy match repairing” (Ortega et al., 2016). With Neural Machine Translation (NMT), the integration of Fuzzy Matching is less obvious since NMT does not keep nor build a database of aligned sequences and does not explicitly use n-gram lan- guage models for decoding. The only obvious and …

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