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Abstract
Conventional retrieval-augmented neural machine translation (RANMT) systems leverage bilingual corpora, e.g., translation memories (TMs). Yet, in many settings, monolingual corpora in the target language are often available. This work explores ways to take advantage of such resources by directly retrieving relevant target language segments, based on a source-side query. For this, we design improved cross-lingual retrieval systems, trained with both sentence level and word-level matching objectives. In our experiments with two RANMT architectures, we assess of such cross-lingual objectives in a controlled setting, reaching performances that match those of standard TM-based models. We also showcase our method on real-world settings, using much larger monolingual corpora, and observe strong improvements over both the baseline setting, and general-purpose cross-lingual retrievers.- Anthology ID:
- 2026.eamt-1.27
- Volume:
- Proceedings of the 26th Annual Conference of the European Association for Machine Translation (Volume 1)
- Month:
- June
- Year:
- 2026
- Address:
- Tilburg, The Netherlands
- Editors:
- Dimitar Shterionov, Eva Vanmassenhove, Mirella De Sisto, Fred Blain, Javad Pourmostafa Roshan Sharami, Lisa Lepp, Chiara Manna, Argentina Anna Rescigno, Alina Karakanta, Ayla Rigouts Terryn, Manuel Lardelli, Natalia Resende, Elena Murgolo, Janiça Hackenbuchner, Anna Zaretskaya, Miquel Esplà-Gomis, Thierry Etchegoyhen, Dagmar Gromann, Rachel Bawden, Barry Haddow, Sara Szoc, Mikel Forcada, Helena Moniz
- Venue:
- EAMT
- SIG:
- Publisher:
- European Association for Machine Translation
- Note:
- Pages:
- 412–431
- Language:
- URL:
- https://aclanthology.org/2026.eamt-1.27/
- DOI:
- Bibkey:
- Cite (ACL):
- Maxime Bouthors, Josep Crego, Dakun Zhang, and François Yvon. 2026. Improving Retrieval-Augmented Neural Machine Translation with Monolingual Data. In Proceedings of the 26th Annual Conference of the European Association for Machine Translation (Volume 1), pages 412–431, Tilburg, The Netherlands. European Association for Machine Translation.
- Cite (Informal):
- Improving Retrieval-Augmented Neural Machine Translation with Monolingual Data (Bouthors et al., EAMT 2026)
- Copy Citation:
- PDF:
- https://aclanthology.org/2026.eamt-1.27.pdf
Export citation
@inproceedings{bouthors-etal-2026-improving,
title = "Improving Retrieval-Augmented Neural Machine Translation with Monolingual Data",
author = "Bouthors, Maxime and
Crego, Josep and
Zhang, Dakun and
Yvon, Fran{\c{c}}ois",
editor = "Shterionov, Dimitar and
Vanmassenhove, Eva and
De Sisto, Mirella and
Blain, Fred and
Pourmostafa Roshan Sharami, Javad and
Lepp, Lisa and
Manna, Chiara and
Rescigno, Argentina Anna and
Karakanta, Alina and
Rigouts Terryn, Ayla and
Lardelli, Manuel and
Resende, Natalia and
Murgolo, Elena and
Hackenbuchner, Jani{\c{c}}a and
Zaretskaya, Anna and
Espl{\`a}-Gomis, Miquel and
Etchegoyhen, Thierry and
Gromann, Dagmar and
Bawden, Rachel and
Haddow, Barry and
Szoc, Sara and
Forcada, Mikel and
Moniz, Helena",
booktitle = "Proceedings of the 26th Annual Conference of the {E}uropean Association for Machine Translation (Volume 1)",
month = jun,
year = "2026",
address = "Tilburg, The Netherlands",
publisher = "European Association for Machine Translation",
url = "https://aclanthology.org/2026.eamt-1.27/",
pages = "412--431",
ISBN = "9789403901411",
abstract = "Conventional retrieval-augmented neural machine translation (RANMT) systems leverage bilingual corpora, e.g., translation memories (TMs). Yet, in many settings, monolingual corpora in the target language are often available. This work explores ways to take advantage of such resources by directly retrieving relevant target language segments, based on a source-side query. For this, we design improved cross-lingual retrieval systems, trained with both sentence level and word-level matching objectives. In our experiments with two RANMT architectures, we assess of such cross-lingual objectives in a controlled setting, reaching performances that match those of standard TM-based models. We also showcase our method on real-world settings, using much larger monolingual corpora, and observe strong improvements over both the baseline setting, and general-purpose cross-lingual retrievers."
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%0 Conference Proceedings %T Improving Retrieval-Augmented Neural Machine Translation with Monolingual Data %A Bouthors, Maxime %A Crego, Josep %A Zhang, Dakun %A Yvon, François %Y Shterionov, Dimitar %Y Vanmassenhove, Eva %Y De Sisto, Mirella %Y Blain, Fred %Y Pourmostafa Roshan Sharami, Javad %Y Lepp, Lisa %Y Manna, Chiara %Y Rescigno, Argentina Anna %Y Karakanta, Alina %Y Rigouts Terryn, Ayla %Y Lardelli, Manuel %Y Resende, Natalia %Y Murgolo, Elena %Y Hackenbuchner, Janiça %Y Zaretskaya, Anna %Y Esplà-Gomis, Miquel %Y Etchegoyhen, Thierry %Y Gromann, Dagmar %Y Bawden, Rachel %Y Haddow, Barry %Y Szoc, Sara %Y Forcada, Mikel %Y Moniz, Helena %S Proceedings of the 26th Annual Conference of the European Association for Machine Translation (Volume 1) %D 2026 %8 June %I European Association for Machine Translation %C Tilburg, The Netherlands %@ 9789403901411 %F bouthors-etal-2026-improving %X Conventional retrieval-augmented neural machine translation (RANMT) systems leverage bilingual corpora, e.g., translation memories (TMs). Yet, in many settings, monolingual corpora in the target language are often available. This work explores ways to take advantage of such resources by directly retrieving relevant target language segments, based on a source-side query. For this, we design improved cross-lingual retrieval systems, trained with both sentence level and word-level matching objectives. In our experiments with two RANMT architectures, we assess of such cross-lingual objectives in a controlled setting, reaching performances that match those of standard TM-based models. We also showcase our method on real-world settings, using much larger monolingual corpora, and observe strong improvements over both the baseline setting, and general-purpose cross-lingual retrievers. %U https://aclanthology.org/2026.eamt-1.27/ %P 412-431
Markdown (Informal)
[Improving Retrieval-Augmented Neural Machine Translation with Monolingual Data](https://aclanthology.org/2026.eamt-1.27/) (Bouthors et al., EAMT 2026)
- Improving Retrieval-Augmented Neural Machine Translation with Monolingual Data (Bouthors et al., EAMT 2026)
ACL
- Maxime Bouthors, Josep Crego, Dakun Zhang, and François Yvon. 2026. Improving Retrieval-Augmented Neural Machine Translation with Monolingual Data. In Proceedings of the 26th Annual Conference of the European Association for Machine Translation (Volume 1), pages 412–431, Tilburg, The Netherlands. European Association for Machine Translation.