Summary of Learning From Others’ Mistakes: Finetuning Machine Translation Models with Span-level Error Annotations, by Lily H. Zhang et al.
Learning from others’ mistakes: Finetuning machine translation models with span-level error annotations
by Lily H. Zhang, Hamid Dadkhahi, Mara Finkelstein, Firas Trabelsi, Jiaming Luo, Markus Freitag
First submitted to arxiv on: 21 Oct 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 Machine learning educators can now expect a significant boost in model quality thanks to a novel approach that incorporates fine-grained span-level annotations. Researchers developed a finetuning algorithm called Training with Annotations (TWA) that leverages targeted error information and flexible learning to improve machine translation models. TWA considers the overall sequence trajectory, allowing it to effectively utilize non-error spans as positive signals. This technique outperforms existing methods on English-German and Chinese-English translation tasks, demonstrating its potential for real-world applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a world where language models can learn from mistakes! A team of researchers has discovered a way to improve machine translation by using feedback from annotated datasets. They created an algorithm called Training with Annotations (TWA) that can learn from small mistakes and correct them. This new approach showed great results on translating English into German and Chinese, making it a promising tool for the future. |
Keywords
» Artificial intelligence » Machine learning » Translation
