Lingua Custodia’s Participation at the WMT 2023 Terminology Shared Task (2023)

Abstract: This paper presents Lingua Custodia’s submission to the WMT23 shared task on Terminology shared task. Ensuring precise translation of technical terms plays a pivotal role in gauging the final quality of machine translation results. Our goal is to follow the terminology constraint while applying the machine translation system. Inspired by the recent work of terminology control, we propose to annotate the machine learning training data by leveraging a synthetic dictionary extracted in a fully non supervised way from the give parallel corpora. The model learned with this training data can then be then used to translate text with a given terminology in a flexible manner. In addition, we introduce a careful annotated data re-sampling step in order to guide the model to see different terminology types enough times. In this task we consider all the three language directions: Chinese to English, English to Czech and German to English. Our automatic evaluation metrics with the submitted systems show the effectiveness of the proposed method.

## introduction it is well proven that modern neural machine translation (nmt) systems @xcite @xcite achieve generally satisfying translation results. nonetheless, the performance of translation with terminology control remains to be improved. this paper describes our submission to the wmt23 terminology translation task in chinese to english, english to czech and german to english direction. the task aims to develop and evaluate machine translation systems which can translate domain specific terms in an accurate and consistent way with some extra terminology information. note that the terminology is provided only in the inference phase, for the training there's no existing resources about the terminology. previous works on machine translation with terminology control can be grouped into two categories according to whether the method needs training the model with terminology information. one group incorporates the constraint during the inference time @xcite @xcite . these methods can typically satisfy most of the constraints but suffer from high computational cost and sometimes low translation quality because it always tries to strictly apply the terminology constraint regardless of the correctness of the whole sentence. the other group integrates lexical constraints during training @xcite @xcite by annotating the data with special tags in order to guide the model to learn the enforcement of the translation constraints. the main disadvantage of these methods is the lack of guarantee of all constraints in the translations. another limitation of these works is that they usually requires a term dictionary to augment the data, such extra resource is not always trivial to obtain for some domains in some languages. our work follows the second line of methods which incorporate terminology in the training by inserting special tags. upon the recent works of @xcite , our system has made several improvements: 1. a terminology extracted from the given training corpus in a full non supervised manner rather than from a supervised approach or a given dictionary like in @xcite . we evaluate our work on all the wmt23 terminology task including the blind test. since the reference is not released by the time of writing this paper, we evaluate our system by a simple naive strict match with respect to the target constraint. our results show the effectiveness of the proposed method. ## method in this section we present out system for the terminology task. our approach is inspired by @xcite and is further developed and adapted to this task. ## conclusion this paper describes our submission to the terminology shared task. we participate in three language directions, german to english, english to czech and chinese to english. we extract a bilingual dictionary for the three language directions in a fully non supervised way and train a neural machine translation model with augmented data for each direction. our term strict match evaluation shows the effectiveness our proposed system for all the three directions.