Papers by Maxime Bouthors
Towards Example-Based NMT with Multi-Levenshtein Transformers (2023.emnlp-main)
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| Challenge: | Retrieval-augmented machine translation (RAMT) is attracting growing attention . it is assumed to implement some form of domain adaptation . |
| Approach: | They propose a retrieval-augmented version of the Levenshtein Transformer to make it more transparent . they propose to perform training and inference in this model, based on multi-way alignment algorithms and imitation learning. |
| Outcome: | The proposed architecture improves translation performance and improves consistency of translations compared to previous models. |
Retrieving Examples from Memory for Retrieval Augmented Neural Machine Translation: A Systematic Comparison (2024.findings-naacl)
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| Challenge: | Existing approaches to extract examples from memory are limited, but the upstream retrieval step is still unexplored. |
| Approach: | They propose to use a standard autoregressive model, edit-based model and a large language model with in-context learning to investigate the effect of retrieval methods on translation scores. |
| Outcome: | The proposed architectures improve translation scores and increase diversity of examples. |