Lingua Custodia's participation at the WMT 2021 Machine Translation using Terminologies shared task
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 11 of its 13 references that are in the map. First posted on arXiv 3 Nov 2021.
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11 of 13 references.
References · 11
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- 2018Fast Lexically Constrained Decoding with Dynamic Beam Allocation for Neural Machine Translationcited by 182
- 2019Training Neural Machine Translation To Apply Terminology Constraintscited by 73
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- 2016SYSTRAN's Pure Neural Machine Translation Systemscited by 24
- 2021On the Evaluation of Machine Translation for Terminology Consistencycited by 15
- 2021Encouraging Neural Machine Translation to Satisfy Terminology Constraintscited by 10
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2 papers.