@inproceedings{hasler-etal-2018-neural,
title = "Neural Machine Translation Decoding with Terminology Constraints",
author = "Hasler, Eva and
de Gispert, Adri{\`a} and
Iglesias, Gonzalo and
Byrne, Bill",
editor = "Walker, Marilyn and
Ji, Heng and
Stent, Amanda",
booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N18-2081",
doi = "10.18653/v1/N18-2081",
pages = "506--512",
abstract = "Despite the impressive quality improvements yielded by neural machine translation (NMT) systems, controlling their translation output to adhere to user-provided terminology constraints remains an open problem. We describe our approach to constrained neural decoding based on finite-state machines and multi-stack decoding which supports target-side constraints as well as constraints with corresponding aligned input text spans. We demonstrate the performance of our framework on multiple translation tasks and motivate the need for constrained decoding with attentions as a means of reducing misplacement and duplication when translating user constraints.",
}
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<abstract>Despite the impressive quality improvements yielded by neural machine translation (NMT) systems, controlling their translation output to adhere to user-provided terminology constraints remains an open problem. We describe our approach to constrained neural decoding based on finite-state machines and multi-stack decoding which supports target-side constraints as well as constraints with corresponding aligned input text spans. We demonstrate the performance of our framework on multiple translation tasks and motivate the need for constrained decoding with attentions as a means of reducing misplacement and duplication when translating user constraints.</abstract>
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%0 Conference Proceedings
%T Neural Machine Translation Decoding with Terminology Constraints
%A Hasler, Eva
%A de Gispert, Adrià
%A Iglesias, Gonzalo
%A Byrne, Bill
%Y Walker, Marilyn
%Y Ji, Heng
%Y Stent, Amanda
%S Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F hasler-etal-2018-neural
%X Despite the impressive quality improvements yielded by neural machine translation (NMT) systems, controlling their translation output to adhere to user-provided terminology constraints remains an open problem. We describe our approach to constrained neural decoding based on finite-state machines and multi-stack decoding which supports target-side constraints as well as constraints with corresponding aligned input text spans. We demonstrate the performance of our framework on multiple translation tasks and motivate the need for constrained decoding with attentions as a means of reducing misplacement and duplication when translating user constraints.
%R 10.18653/v1/N18-2081
%U https://aclanthology.org/N18-2081
%U https://doi.org/10.18653/v1/N18-2081
%P 506-512
Markdown (Informal)
[Neural Machine Translation Decoding with Terminology Constraints](https://aclanthology.org/N18-2081) (Hasler et al., NAACL 2018)
ACL
- Eva Hasler, Adrià de Gispert, Gonzalo Iglesias, and Bill Byrne. 2018. Neural Machine Translation Decoding with Terminology Constraints. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pages 506–512, New Orleans, Louisiana. Association for Computational Linguistics.