Summary of Efficient Minimum Bayes Risk Decoding Using Low-rank Matrix Completion Algorithms, by Firas Trabelsi and David Vilar and Mara Finkelstein and Markus Freitag
Efficient Minimum Bayes Risk Decoding using Low-Rank Matrix Completion Algorithms
by Firas Trabelsi, David Vilar, Mara Finkelstein, Markus Freitag
First submitted to arxiv on: 5 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 presents a novel approach for approximating Minimum Bayes Risk (MBR) decoding, a powerful strategy used for text generation tasks like machine translation. The authors formulate MBR decoding as a matrix completion problem, where utility metric scores between candidate hypotheses and pseudo-reference translations form a low-rank matrix. By exploiting this structure, they propose an efficient approximation method using the Alternating Least Squares (ALS) algorithm, which reduces computational complexity by 16 times while maintaining translation quality. The proposed method achieves equal quality to vanilla MBR decoding on machine translation tasks, as measured by COMET22 on the WMT22 dataset (en<>de and en<>ru), outperforming other approximation methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper makes it possible for text generation machines to work better. It helps solve a problem called Minimum Bayes Risk decoding, which is important for things like machine translation. The scientists turn this problem into another kind of math problem called matrix completion. They find that the scores between different options and a “best” answer form a special pattern. By using a new way of solving this math problem, they can make it happen much faster while still getting the same good results. This helps machines translate text better. |
Keywords
» Artificial intelligence » Text generation » Translation
