通过语义相似度与模型置信度，提前预测对话回复以降低用户等待时间。
Dialogue Response Prefetching Based on Semantic Similarity and Prediction Confidence of Language Model
- 基于语义相似度和预测置信度判断是否可提前预取回复。
- 在真实对话数据上验证，显著降低用户感知延迟。
- 适合语音对话系统优化，提升交互流畅性。
为降低语音对话系统中用户的感知延迟（UPL，即用户等待系统响应的时间），研究了对话回复的预取机制。为了减少延迟，需在用户说话结束前，通过语言模型预测完整的用户语句，从而提前准备回复。本文提出一种预测置信度模型（PCM），通过估算预测的完整用户语句与实际完整语句之间的语义相似度，来判断是否具备预取条件。我们在真实对话数据上评估了该模型，结果表明其能有效识别可预取场景，从而减少用户等待时间。
原文摘要 · Abstract (English)
Prefetching of dialogue responses has been investigated to reduce user-perceived latency (UPL), which refers to the user's waiting time before receiving the system's response, in spoken dialogue systems. To reduce the UPL, it is necessary to predict complete user utterances before the end of the user's speech, typically by language models, to prepare prefetched dialogue responses. In this study, we proposed a prediction confidence model (PCM) that determines whether prefetching is possible or not by estimating the semantic similarity between the predicted complete user utterance and the complete user utterance. We evaluated our PCM based on the differences between the predicted complete user utterance and the complete user utterance.
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