Bridging Domains for Automatic Post-Editing: A Classifier-Guided Multi-Domain Adaptation Framework

Sourabh Deoghare, Diptesh Kanojia, Pushpak Bhattacharyya


Abstract
Automatic Post-Editing (APE) is a widely studied approach for enhancing the output quality of Neural Machine Translation (NMT) systems. While most prior work has focused on general-purpose APE, the potential of domain-specific APE, such as for personalized or specialized content, remains underexplored due to the scarcity of domain-labeled training data. In this work, we investigate domain adaptation for APE using adapter-based methods. Our proposed multitask learning-based domain adaptation framework includes the use of a domain classifier to get a weighted combination of parallel domain-specific adapters at inference time, without requiring prior domain knowledge. This design allows the model to leverage cross-domain similarities, making it especially robust in low-resource domain scenarios. Our experimental results on English–German, English–Marathi, and English–Tamil pairs across different domains for each pair show substantial improvements over their respective general-purpose APE baselines. To facilitate further research, we will release human-annotated domain labels for triplets in WMT22 English–Marathi, and WMT24 English–Tamil APE datasets and the code.
Anthology ID:
2026.eamt-1.31
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:
496–514
Language:
URL:
https://aclanthology.org/2026.eamt-1.31/
DOI:
Bibkey:
Cite (ACL):
Sourabh Deoghare, Diptesh Kanojia, and Pushpak Bhattacharyya. 2026. Bridging Domains for Automatic Post-Editing: A Classifier-Guided Multi-Domain Adaptation Framework. In Proceedings of the 26th Annual Conference of the European Association for Machine Translation (Volume 1), pages 496–514, Tilburg, The Netherlands. European Association for Machine Translation.
Cite (Informal):
Bridging Domains for Automatic Post-Editing: A Classifier-Guided Multi-Domain Adaptation Framework (Deoghare et al., EAMT 2026)
Copy Citation:
PDF:
https://aclanthology.org/2026.eamt-1.31.pdf