Summary of Ca*: Addressing Evaluation Pitfalls in Computation-aware Latency For Simultaneous Speech Translation, by Xi Xu and Wenda Xu and Siqi Ouyang and Lei Li
CA*: Addressing Evaluation Pitfalls in Computation-Aware Latency for Simultaneous Speech Translation
by Xi Xu, Wenda Xu, Siqi Ouyang, Lei Li
First submitted to arxiv on: 21 Oct 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 This paper investigates the long-standing issue of unrealistically high latency measurements in simultaneous speech translation (SimulST) systems, particularly in unsegmented streaming settings. The authors reveal that this phenomenon is caused by a fundamental misconception underlying existing latency evaluation approaches and demonstrate its impact on both segment-level and streaming-based metrics. To address these limitations, the paper proposes a modification to correctly measure computation-aware latency for SimulST systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks into why some simultaneous speech translation systems are showing really high delays when translating spoken words in real-time. The problem is that current methods of measuring delay aren’t accurate because they’re based on a misunderstanding. The authors show how this affects not just streaming translations but also individual sentences. To fix the issue, they suggest a new way to measure delay that takes into account how the system processes information. |
Keywords
» Artificial intelligence » Translation
