SYSTRAN @ WMT 2021: Terminology Task (2021)

Abstract: This paper describes SYSTRAN submissions to the WMT 2021 terminology shared task. We participate in the English-to-French translation direction with a standard Transformer neural machine translation network that we enhance with the ability to dynamically include terminology constraints, a very common industrial practice. Two state-of-the-art terminology insertion methods are evaluated based (i) on the use of placeholders complemented with morphosyntactic annotation and (ii) on the use of target constraints injected in the source stream. Results show the suitability of the presented approaches in the evaluated scenario where terminology is used in a system trained on generic data only.

## introduction the high quality obtained by out-of-the-box neural machine translation (nmt) systems @xcite has boosted the adoption 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 @xcite @xcite , terminology handling @xcite @xcite , interactive translation @xcite , post-editing modelling @xcite or dynamic adaptation @xcite are all different techniques to make machine translation part of reallife localization workflow. terminology resources with all their sophistication have been the core building bricks and a continuous challenge to acquire in volume @xcite for rule-based engines. at the other extreme, they have been reduced to corpus or aligned "phrase pairs" @xcite 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 the one hand, it has access to deep actual linguistic knowledge @xcite and demonstrates a huge ability to generalize. but on the other hand, results are more difficult to interpret @xcite , and subsequently the translation process is far more complicated to control. therefore, as for several other linguistic annotations, the challenge is how terminological information can be "passed" to the model. 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 and context. inline with the conditions of the wmt 2021 terminology shared task, we present english-to-french nmt engines built from abundant generic (out-of-domain) training data. we evaluate several methods to enhance translation engines with the ability to integrate terminology as a quick way to dynamically specialize a translation to a particular domain, which in this case considers the new covid-19 domain and the large efforts for translation of critical information regarding pandemic handling and infection prevention strategies. indomain resources are limited to word-and phraselevel terminology entries created to guide professional translators to ensure both accuracy and consistency in translations. our generic systems make only use of terminologies at inference time. the remainder of the paper is organized as follows: section 2 gives details of several terminology injection approaches considered in this work. in addition, we outline a grammatical error correction network that is applied over french translation hypotheses. the experimental framework is presented in section 3. results are discussed in section 4. finally, we draw conclusions in section 5. ## terminology injection terminology is typically defined as the technical or special terms used in a business, art, science, or special subject. a high quality asset maintained by language specialists as part of a translation project. it allows to guarantee language consistency, certify translation accuracy and define constraints to human translation. in recent years there has been significant work proposing methods to integrate such external specialized terminologies into nmt models, each showing different levels of performance when facing terminology injection issues, mainly inference overhead and generalization power. in this section we describe the two main methods employed for this shared task and illustrate the particularities of each on a common scenario using two english-french terminology entries: coronavirus ↝ coronavirus and pneumonia ↝ pneumonie, in the following translation: coronavirus can cause pneumonia les coronavirus peuvent causer des pneumonies ## results we now evaluate the performance of matching terminology entries over the development input sentences. note that the same matching method is always applied, detailed in section 2.1, where input sentences are matched against all possible inflections of source terms. ## conclusions we presented systran english-to-french submissions to wmt 2021 terminology shared task. all our systems follow the transformer network architecture enhanced with the ability to dynamically include terminology constraints. several terminology injection methods were evaluated, showing their ability to effectively injecting terms while producing highly accurate translations.