论文标题
预测实体流行，以改善虚拟助手的口语实体识别
Predicting Entity Popularity to Improve Spoken Entity Recognition by Virtual Assistants
论文作者
论文摘要
我们专注于提高虚拟助手（VA）在识别口头查询中新兴实体方面的有效性。我们介绍了一种使用历史用户交互的方法来预测哪些实体将在哪些实体中获得流行并成为趋势，然后将预测整合到VA的自动语音识别（ASR）组件中。实验表明，我们提出的方法导致新兴实体名称话语的错误相对减少20％，而不会降低系统的整体识别质量。
We focus on improving the effectiveness of a Virtual Assistant (VA) in recognizing emerging entities in spoken queries. We introduce a method that uses historical user interactions to forecast which entities will gain in popularity and become trending, and it subsequently integrates the predictions within the Automated Speech Recognition (ASR) component of the VA. Experiments show that our proposed approach results in a 20% relative reduction in errors on emerging entity name utterances without degrading the overall recognition quality of the system.
