Summary of Unveiling the Power Of Source: Source-based Minimum Bayes Risk Decoding For Neural Machine Translation, by Boxuan Lyu et al.
Unveiling the Power of Source: Source-based Minimum Bayes Risk Decoding for Neural Machine Translation
by Boxuan Lyu, Hidetaka Kamigaito, Kotaro Funakoshi, Manabu Okumura
First submitted to arxiv on: 17 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 novel approach to neural machine translation (NMT), introducing Minimum Bayes Risk (MBR) decoding as an alternative to Maximum a Posteriori (MAP) decoding. MAP aims to maximize the estimated posterior probability, but high probabilities do not always translate to high-quality translations. MBR seeks hypotheses with the highest expected utility, offering a new perspective on NMT. The method is benchmarked against existing approaches and demonstrates improved performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper tries to improve neural machine translation by using a different way of choosing the best translation. Right now, we use a method called Maximum a Posteriori that chooses the answer with the highest probability. But sometimes this doesn’t mean the best translation is chosen. The new method, Minimum Bayes Risk, tries to pick the best translation based on how good it is expected to be. This can lead to better translations. |
Keywords
» Artificial intelligence » Probability » Translation
