@inproceedings{bane-etal-2023-coming,
title = "Coming to Terms with Glossary Enforcement: A Study of Three Approaches to Enforcing Terminology in {NMT}",
author = "Bane, Fred and
Zaretskaya, Anna and
Mir{\'o}, T{\`a}nia Blanch and
Uguet, Celia Soler and
Torres, Jo{\~a}o",
editor = "Nurminen, Mary and
Brenner, Judith and
Koponen, Maarit and
Latomaa, Sirkku and
Mikhailov, Mikhail and
Schierl, Frederike and
Ranasinghe, Tharindu and
Vanmassenhove, Eva and
Vidal, Sergi Alvarez and
Aranberri, Nora and
Nunziatini, Mara and
Escart{\'\i}n, Carla Parra and
Forcada, Mikel and
Popovic, Maja and
Scarton, Carolina and
Moniz, Helena",
booktitle = "Proceedings of the 24th Annual Conference of the European Association for Machine Translation",
month = jun,
year = "2023",
address = "Tampere, Finland",
publisher = "European Association for Machine Translation",
url = "https://aclanthology.org/2023.eamt-1.34",
pages = "345--353",
abstract = "Enforcing terminology constraints is less straight-forward in neural machine translation (NMT) than statistical machine translation. Current methods, such as alignment-based insertion or the use of factors or special tokens, each have their strengths and drawbacks. We describe the current state of research on terminology enforcement in transformer-based NMT models, and present the results of our investigation into the performance of three different approaches. In addition to reference based quality metrics, we also evaluate the linguistic quality of the translations thus produced. Our results show that each approach is effective, though a negative impact on translation fluency remains evident.",
}
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<abstract>Enforcing terminology constraints is less straight-forward in neural machine translation (NMT) than statistical machine translation. Current methods, such as alignment-based insertion or the use of factors or special tokens, each have their strengths and drawbacks. We describe the current state of research on terminology enforcement in transformer-based NMT models, and present the results of our investigation into the performance of three different approaches. In addition to reference based quality metrics, we also evaluate the linguistic quality of the translations thus produced. Our results show that each approach is effective, though a negative impact on translation fluency remains evident.</abstract>
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%0 Conference Proceedings
%T Coming to Terms with Glossary Enforcement: A Study of Three Approaches to Enforcing Terminology in NMT
%A Bane, Fred
%A Zaretskaya, Anna
%A Miró, Tània Blanch
%A Uguet, Celia Soler
%A Torres, João
%Y Nurminen, Mary
%Y Brenner, Judith
%Y Koponen, Maarit
%Y Latomaa, Sirkku
%Y Mikhailov, Mikhail
%Y Schierl, Frederike
%Y Ranasinghe, Tharindu
%Y Vanmassenhove, Eva
%Y Vidal, Sergi Alvarez
%Y Aranberri, Nora
%Y Nunziatini, Mara
%Y Escartín, Carla Parra
%Y Forcada, Mikel
%Y Popovic, Maja
%Y Scarton, Carolina
%Y Moniz, Helena
%S Proceedings of the 24th Annual Conference of the European Association for Machine Translation
%D 2023
%8 June
%I European Association for Machine Translation
%C Tampere, Finland
%F bane-etal-2023-coming
%X Enforcing terminology constraints is less straight-forward in neural machine translation (NMT) than statistical machine translation. Current methods, such as alignment-based insertion or the use of factors or special tokens, each have their strengths and drawbacks. We describe the current state of research on terminology enforcement in transformer-based NMT models, and present the results of our investigation into the performance of three different approaches. In addition to reference based quality metrics, we also evaluate the linguistic quality of the translations thus produced. Our results show that each approach is effective, though a negative impact on translation fluency remains evident.
%U https://aclanthology.org/2023.eamt-1.34
%P 345-353
Markdown (Informal)
[Coming to Terms with Glossary Enforcement: A Study of Three Approaches to Enforcing Terminology in NMT](https://aclanthology.org/2023.eamt-1.34) (Bane et al., EAMT 2023)
ACL