Summary of Signbleu: Automatic Evaluation Of Multi-channel Sign Language Translation, by Jung-ho Kim et al.
SignBLEU: Automatic Evaluation of Multi-channel Sign Language Translation
by Jung-Ho Kim, Mathew Huerta-Enochian, Changyong Ko, Du Hui Lee
First submitted to arxiv on: 10 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 introduces a novel task called multi-channel sign language translation (MCSLT) and proposes a new metric, SignBLEU, to capture multiple signal channels in automatic sign language translation. The authors validate SignBLEU on three sign language corpora with varied linguistic structures and transcription methodologies, finding that it consistently correlates better with human judgment than competing metrics. To facilitate further research, the paper provides benchmark scores for the corpora and releases the source code for SignBLEU. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Sign languages are complex languages that use hand movements, facial expressions, and body language to communicate. Right now, automatic sign language translation only looks at hand movements and ignores other important signals. This can lead to lost information and confusion. The paper creates a new task called multi-channel sign language translation (MCSLT) and a special way to measure how well it works called SignBLEU. They tested SignBLEU on three different datasets of sign languages and found that it does a better job than other methods at guessing what the translation should be. |
Keywords
» Artificial intelligence » Translation
