Integrating Domain Terminology into Neural Machine Translation

Proceedings of the 28th International Conference on Computational Linguistics, pages 3925–3937 Barcelona, Spain (Online), December 8-13, 2020 3925 Integrating Domain Terminology into Neural Machine Translation Elise Michon, Josep Crego, Jean Senellart SYSTRAN, 5 rue Feydeau, 75002 Paris, France firstname.lastname@systrangroup.com Abstract This paper extends existing work on terminology integration into Neural Machine Translation, a common industrial practice to dynamically adapt translation to a specific domain. Our method, based on the use of placeholders complemented with morphosyntactic annotation, efficiently taps into the ability of the neural network to deal with symbolic knowledge to surpass the surface gen- eralization shown by alternative techniques. We compare our approach to state-of-the-art systems and benchmark them through a well-defined evaluation framework, focusing on actual applica- tion of terminology and not just on the overall performance. Results indicate the suitability of our method in the use-case where terminology is used in a system trained on generic data only. 1 Introduction High out-of-the-box quality for Neural Machine Translation (Bojar et al., 2016) has boosted the adop- tion of automatic translation by the industry and invigorated the research and development on domain adaption and integration of technology in human translation workflows. For instance, combination with translation memories (Bult´e and Tezcan, 2019; Xu et al., 2020), terminology handling (Hasler et al., 2018; Dinu et al., 2019), interactive translation (Peris and Casacuberta, 2019), post-editing modelling (Chatterjee et al., 2019) or dynamic adaptation (Farajian et al., 2017) are all different techniques to make machine translation part of real-life localization workflow. In this work, we focus on integrating terminology as a quick way to dynamically specialize a transla- tion to a specific domain. Terminology is a key high quality asset maintained by language specialists as part of a translation project: it is a way to guarantee language consistency, certify translation accuracy and define constraints to human translation. Terminologists are putting a lot of effort to describe terms, including their morphology, their syntax, the semantic context in which these terms apply, etc. From a human perspective, even though presentation and usage of dictionaries have evolved from ontology (as found in paper dictionary) to corpus-based presentation, looking up terms in a dictionary is the ultimate point of reference for validating the correct term for a specific domain in a specific context. Terminology resources with all their sophistication have been the core building bricks and a continuous challenge to acquire in volume (Senellart et al., 2003) for rule-based engines. At the other extreme, they have been reduced to corpus or aligned “phrases” (Schwenk et al., 2008) for Statistical Machine Translation approaches, missing most of their intrinsic linguistic properties. In contrast, Neural Machine Translation operates on word and sentence representations in a continuous space so, on one hand, has access to deep actual linguistic knowledge (Conneau et al., 2018) and demonstrates a huge ability to generalize. But on the other hand, results are more difficult to interpret (Koehn and Knowles, 2017), and subsequently the translation process is far more complicated to control. Therefore, as for several other …

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