Summary of Chasing Comet: Leveraging Minimum Bayes Risk Decoding For Self-improving Machine Translation, by Kamil Guttmann et al.
Chasing COMET: Leveraging Minimum Bayes Risk Decoding for Self-Improving Machine Translation
by Kamil Guttmann, Mikołaj Pokrywka, Adrian Charkiewicz, Artur Nowakowski
First submitted to arxiv on: 20 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a Minimum Bayes Risk (MBR) decoding approach for self-improvement in machine translation, focusing on domain adaptation and low-resource languages. The method fine-tunes the model on its own forward translations, using COMET as the MBR utility metric to rerank translations that better align with human preferences. The iterative application of this approach is explored, including potential language-specific MBR utility metrics. Results show significant enhancements in translation quality for all examined language pairs, including domain-adapted models and low-resource settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps machine translators get better at translating languages they’re not familiar with. It uses a special way to adjust translations based on how well humans like them. The method makes the translator better over time, especially when working with languages that are hard to learn. The results show that this approach works well for different language pairs and even for adapting to new domains or learning from small amounts of data. |
Keywords
» Artificial intelligence » Domain adaptation » Translation
