Summary of Improving Statistical Significance in Human Evaluation Of Automatic Metrics Via Soft Pairwise Accuracy, by Brian Thompson and Nitika Mathur and Daniel Deutsch and Huda Khayrallah
Improving Statistical Significance in Human Evaluation of Automatic Metrics via Soft Pairwise Accuracy
by Brian Thompson, Nitika Mathur, Daniel Deutsch, Huda Khayrallah
First submitted to arxiv on: 15 Sep 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 proposed paper introduces a new meta-metric called Soft Pairwise Accuracy (SPA) to compare human judgments with automatic metric scores in natural language processing tasks. The authors highlight the limitations of existing methods, including Pairwise Accuracy (PA), which can lead to artificial ties among metrics due to the limited number of output values. SPA addresses this issue by incorporating statistical significance into the comparison process, making it more stable and discriminative than PA. The paper demonstrates the effectiveness of SPA through experiments and its selection as the official system-level metric for the 2024 WMT Metrics Shared Task. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers created a new way to measure how well automatic metrics match human judgments. This is important because different metrics can be used to evaluate language processing tasks, but there’s no clear definition of what “best” means. They introduced Soft Pairwise Accuracy (SPA), which combines the accuracy of each metric with statistical significance. SPA shows more stable results and can distinguish between metrics better than existing methods. The paper also highlights the limitations of current methods and how SPA addresses these issues. |
Keywords
* Artificial intelligence * Natural language processing
