Summary of Advancing Explainability in Neural Machine Translation: Analytical Metrics For Attention and Alignment Consistency, by Anurag Mishra
Advancing Explainability in Neural Machine Translation: Analytical Metrics for Attention and Alignment Consistency
by Anurag Mishra
First submitted to arxiv on: 24 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 proposed framework quantitatively evaluates the explainability of Neural Machine Translation (NMT) model attention patterns by comparing them against statistical alignments and correlating them with standard machine translation quality metrics. The study introduces a set of metrics, including attention entropy and alignment agreement, and validates them on an English-German test subset from WMT14 using a pre-trained mT5 model. The results show that sharper attention distributions correlate with improved interpretability, but do not always guarantee better translation quality. This work advances our understanding of NMT explainability and guides future efforts toward building more transparent and reliable machine translation systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary NMT models are really good at translating text from one language to another, but they don’t always make sense or behave as we want them to. To fix this, researchers need to understand how these models work inside. In this study, scientists created a special way to measure how well NMT models explain their decisions when translating text. They tested it on English-German translations and found that when the model’s “attention” (its focus on certain parts of the text) is clear and sharp, it leads to better translations. This research helps us understand why NMT models work the way they do and how we can make them more reliable. |
Keywords
» Artificial intelligence » Alignment » Attention » Translation
