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.

Lingua Custodia's Participation at the WMT 2022 Word-Level Auto-completion Shared Task

This paper describes Lingua Custodia's submission to the WMT21 shared task on machine translation using terminologies. 
We consider three directions, namely English to French, Russian, and Chinese.
We rely on a Transformer-based architecture as a building block, and we explore a method which introduces two main changes to the standard procedure to handle terminologies. The first one consists in augmenting the training data in such a way as to encourage the model to learn a copy behavior when it encounters terminology constraint terms. The second change is constraint token masking, whose purpose is to ease copy behavior learning and to improve model generalization. 
Empirical results show that our method satisfies most terminology constraints while maintaining high translation quality.

Lingua Custodia's participation at the WMT 2021 Machine Translation using Terminologies shared task

We present a new approach to encourage neural machine translation to satisfy lexical constraints. Our method acts at the training step and thereby avoiding the introduction of any extra computational overhead at inference step. The proposed method combines three main ingredients. The first one consists in augmenting the training data to specify the constraints. Intuitively, this encourages the model to learn a copy behavior when it encounters constraint terms. Compared to previous work, we use a simplified augmentation strategy without source factors. The second ingredient is constraint token masking, which makes it even easier for the model to learn the copy behavior and generalize better. The third one, is a modification of the standard cross entropy loss to bias the model towards assigning high probabilities to constraint words. Empirical results show that our method improves upon related baselines in terms of both BLEU score and the percentage of generated constraint terms.

Encouraging Neural Machine Translation to Satisfy Terminology Constraints

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W3 (WMT) Seventh Conference on Machine Translation 

workshop paper

## Welcome to EMNLP 2022!
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W11: Sixth Conference on Machine Translation Day 1

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EMNLP 2021

EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible.

Findings Papers - Machine Translation and Multilinguality

technical paper

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IJCNLP-AACL 2021

The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP).

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Lingua Custodia's Participation at the WMT 2022 Word-Level Auto-completion Shared Task

Lingua Custodia's participation at the WMT 2021 Machine Translation using Terminologies shared task

Encouraging Neural Machine Translation to Satisfy Terminology Constraints

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