Encouraging Neural Machine Translation to Satisfy Terminology Constraints.

Melissa Ailem, Jingshu Liu, Raheel Qader

Abstract
Encouraging Neural Machine Translation to Satisfy Terminology Constraints. 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.
Anthology ID:
2022.jeptalnrecital-taln.44
Volume:
Actes de la 29e Conférence sur le Traitement Automatique des Langues Naturelles. Volume 1 : conférence principale
Month:
6
Year:
2022
Address:
Avignon, France
Editors:
Yannick Estève, Tania Jiménez, Titouan Parcollet, Marcely Zanon Boito
Venue:
JEP/TALN/RECITAL
SIG:
Publisher:
ATALA
Note:
Pages:
446–446
Language:
URL:
https://aclanthology.org/2022.jeptalnrecital-taln.44
DOI:
Bibkey:
Cite (ACL):
Melissa Ailem, Jingshu Liu, and Raheel Qader. 2022. Encouraging Neural Machine Translation to Satisfy Terminology Constraints.. In Actes de la 29e Conférence sur le Traitement Automatique des Langues Naturelles. Volume 1 : conférence principale, pages 446–446, Avignon, France. ATALA.
Cite (Informal):
Encouraging Neural Machine Translation to Satisfy Terminology Constraints. (Ailem et al., JEP/TALN/RECITAL 2022)
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