In this episode, we discuss Towards Achieving Human Parity on End-to-end Simultaneous Speech Translation via LLM Agent by Shanbo Cheng, Zhichao Huang, Tom Ko, Hang Li, Ningxin Peng, Lu Xu, Qini Zhang. The paper introduces CLASI, a high-quality and human-like Simultaneous Speech Translation (SiST) system inspired by professional interpreters' strategies to balance translation quality and latency. Utilizing a multi-modal retrieving module and Large Language Models (LLMs), CLASI significantly outperforms other systems, especially in challenging real-world scenarios. Evaluated using the valid information proportion (VIP) metric, CLASI achieves impressive results compared to state-of-the-art systems, with VIP scores of 81.3% for Chinese-to-English and 78.0% for English-to-Chinese translations.
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