Summary of Beyond Correlation: Interpretable Evaluation Of Machine Translation Metrics, by Stefano Perrella et al.
Beyond Correlation: Interpretable Evaluation of Machine Translation Metrics
by Stefano Perrella, Lorenzo Proietti, Pere-Lluís Huguet Cabot, Edoardo Barba, Roberto Navigli
First submitted to arxiv on: 7 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 interpretable evaluation framework for Machine Translation (MT) metrics addresses challenges in assessing translation quality and making informed design choices. The framework evaluates MT metrics’ performance using Precision, Recall, and F-score in two scenarios simulating data filtering and translation re-ranking use cases. This approach provides clearer insights into metric capabilities compared to traditional correlation with human judgments. The evaluation also raises concerns about the reliability of manually curated data following DA+SQM guidelines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine Translation (MT) metrics help evaluate how well computers translate languages. Researchers are using these metrics for new tasks, like filtering data and ranking translations. But, current metrics give scores as numbers that are hard to understand, making it difficult to make good choices. Also, the way we usually test MT metrics is by comparing them to what humans think is a good translation. This method isn’t very helpful when trying to figure out how well a metric will work in new situations. To solve these problems, this paper introduces an easy-to-understand framework for evaluating MT metrics. The framework looks at how well metrics do in two scenarios that mimic the data filtering and translation re-ranking tasks. By using Precision, Recall, and F-score, this approach gives better insights into a metric’s capabilities than just comparing it to human judgments. |
Keywords
» Artificial intelligence » Precision » Recall » Translation
