Improving Retrieval-Augmented Neural Machine Translation with Monolingual Data

• URL: http://arxiv.org/abs/2504.21747v1
• Date: Wed, 30 Apr 2025 15:41:03 GMT
• Title: Improving Retrieval-Augmented Neural Machine Translation with Monolingual Data
• Authors: Maxime Bouthors, Josep Crego, François Yvon,
• Abstract summary: In many settings, in-domain monolingual target-side corpora are often available.<n>This work explores ways to take advantage of such resources by retrieving relevant segments directly in the target language.<n>In experiments with two RANMT architectures, we first demonstrate the benefits of such cross-lingual objectives in a controlled setting.<n>We then showcase our method on a real-world set-up, where the target monolingual resources far exceed the amount of parallel data.
• Score: 9.67203800171351
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Conventional retrieval-augmented neural machine translation (RANMT) systems leverage bilingual corpora, e.g., translation memories (TMs). Yet, in many settings, in-domain monolingual target-side corpora are often available. This work explores ways to take advantage of such resources by retrieving relevant segments directly in the target language, 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 first demonstrate the benefits of such cross-lingual objectives in a controlled setting, obtaining translation performances that surpass standard TM-based models. We then showcase our method on a real-world set-up, where the target monolingual resources far exceed the amount of parallel data and observe large improvements of our new techniques, which outperform both the baseline setting, and general-purpose cross-lingual retrievers.

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