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A Context-aware Framework for Translation-mediated Conversations

Researchers José Pombal, Sweta Agrawal, Emmanouil Zaranis, Patrick Fernandes, and André F. T. Martins published a framework in Transactions of the Association for Computational Linguistics Volume 14 (pages 562–587) that improves large language model-based translation by incorporating contextual information during training and inference in bilingual conversational settings. The system produced by the framework, TowerChat, consistently outperformed state-of-the-art systems GPT-4o and TowerInstruct on automatic translation quality metrics across several language pairs in two task-oriented domains: customer chat and user-assistant interaction. The authors also report that the resulting model uses context in an intended and interpretable way, improving consistency between the conveyed message and the generated translations.

read1 min views1 publishedOct 7, 2026
A Context-aware Framework for Translation-mediated Conversations
Image: Aclanthology (auto-discovered)
Abstract

Automatic translation systems offer a powerful solution to bridge language barriers in scenarios where participants do not share a common language. However, these systems can introduce errors leading to misunderstandings and conversation breakdown. A key issue is that current systems fail to incorporate the rich contextual information necessary to resolve ambiguities and omitted details, resulting in literal, inappropriate, or misaligned translations. In this work, we present a framework to improve large language model-based translation systems by incorporating contextual information in bilingual conversational settings during training and inference. We validate our proposed framework on two task-oriented domains: customer chat and user-assistant interaction. Across both settings, the system produced by our framework—TowerChat—consistently results in better translations than state-of-the-art systems like GPT-4o and TowerInstruct, as measured by multiple automatic translation quality metrics on several language pairs. We also show that the resulting model leverages context in an intended and interpretable way, improving consistency between the conveyed message and the generated translations.1

- Anthology ID:
- 2026.tacl-1.26
- Volume:
- [Transactions of the Association for Computational Linguistics, Volume 14](https://aclanthology.org/volumes/2026.tacl-1/)
- Month:
- Year:
  • 2026
  • Address:
  • Cambridge, MA
- Venue:
- [TACL](https://aclanthology.org/venues/tacl/)
- SIG:
- Publisher:
  • MIT Press
- Note:
- Pages:
  • 562–587
- Language:
- URL:
- [https://aclanthology.org/2026.tacl-1.26/](https://aclanthology.org/2026.tacl-1.26/)
- DOI:
- [10.1162/tacl.a.639](https://doi.org/10.1162/tacl.a.639)
- Cite (ACL):
- Cite (Informal):
- [A Context-aware Framework for Translation-mediated Conversations](https://aclanthology.org/2026.tacl-1.26/) (Pombal et al., TACL 2026)
- PDF:
- [https://aclanthology.org/2026.tacl-1.26.pdf](https://aclanthology.org/2026.tacl-1.26.pdf)
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