Lingua Custodia’s Participation at the WMT 2022 Word-Level Auto-completion Shared Task (2022)

Abstract: This paper presents Lingua Custodia’s submission to the WMT22 shared task on Word Level Auto-completion (WLAC). We consider two directions, namely German-English and English-German.The WLAC task in Neural Machine Translation (NMT) consists in predicting a target word given few human typed characters, the source sentence to translate, as well as some translation context. Inspired by recent work in terminology control, we propose to treat the human typed sequence as a constraint to predict the right word starting by the latter. To do so, the source side of the training data is augmented with both the constraints and the translation context. In addition, following new advances in WLAC, we use a joint optimization strategy taking into account several types of translation context. The automatic as well as human accuracy obtained with the submitted systems show the effectiveness of the proposed method.

## introduction modern advances in neural machine translation (nmt) @xcite @xcite gave rise to a new era, where the translation quality significantly surpasses previous statistical machine translation (smt) models @xcite @xcite . although these approaches generate high quality translations, there is still a long way to go towards meeting human quality. in fact, nmt models can still generate several types of grammatical and/or semantic mistakes, which is not tolerated in scenarios requiring accurate and prompt translations. these scenarios include for instance the translations of legal and financial documents, where mistakes are not permitted and can be costly. to overcome this issue, several computer-aided translation (cat) systems have been proposed @xcite to refine nmt models. cat tools include for instance automatic post-edition @xcite @xcite , terminology control @xcite @xcite and sentence vs word-level autocompletion @xcite @xcite . figure 1 : given the source sentence to translate, the translation contexts, and the human typed characters, the wlac task aims to predict a target word starting by the human typed sequence. as illustrated, the word to predict is not necessarily consecutive to the left and right contexts. the current shared task is on word-level auto-completion (wlac) methods, whose objective, as illustrated in figure 1 , is to predict a target word given a source sentence, a translation context, and at least one human typed character. wlac is a central computer-aided task as it helps human translators generate diverse translations quickly and effectively. unfortunately, due to the lack of benchmark datasets, very little work has considered this task. existing methods include the work of @xcite , where the authors leverage the source sen-tence as well as human typed characters to predict the target word. more recently, @xcite proposed to use context information in addition to human-typed characters and source sentence. furthermore, the authors presented a generic procedure to simulate wlac data from any parallel translation datasets, and proposed the first public benchmark for this task. the benchmark dataset contains several types of contexts and therefore a joint optimization strategy is used to take into account all context types during training. we participate in two directions, namely english-german (en-de), german-english (de-en), and we submitted four systems, two for each language direction. following previous work @xcite , our method leverages source sentence, translation context as well as human-typed characters, and it uses a joint objective function to learn model parameters on different types of contexts simultaneously. furthermore, inspired by recent progress in terminology control (tc) for nmt @xcite , we propose a new wlac method that treats the human typed sequence as a constraint to generate the right word. to do so, we augment our training data with translation context as well as human typed characters (constraints). we use tags where needed to distinguish these terms from source tokens. the rest of the paper is organized as follows. section 2 describes the details of our system, section 3 presents the data, while section 4 shows the different experimental settings and results. ## method herein we present our wlac approach which is inspired by recent advances in this task @xcite as well as recent work on terminology control @xcite . ## data we participate in two directions, namely english-german and german-english. we use the parallel source seebarsch gebacken auf seinem rücken , fein zerschnippelter porree und zitronenmelissekraut . english-german data provided by the wlac task consisting of almost 4.5m parallel sentences. following task instructions, we use the script proposed in @xcite to simulate the wlac training data from the provided classical translation data. ## conclusion this paper describes our submission to the wlac shared task. we participate in two language directions, en-de and de-en, and submitted two systems for each direction. for each direction, the first system is trained using initial data provided by the task, while the second system is trained on cleaned data. the evaluation in terms of accuracy shows the effectiveness of the proposed method. furthermore, a significant improvement of accuracy is observed when the number of human typed characters is greater than 1, suggesting that entering at least two characters restrain the search space and improve the chances to predict the right word.