论文标题
噪音强大的语音助手命名实体理解
Noise Robust Named Entity Understanding for Voice Assistants
论文作者
论文摘要
命名的实体识别（NER）和链接（EL）的实体在语音助手互动中起着至关重要的作用，但由于与口语用户查询相关的特殊困难，这是具有挑战性的。在本文中，我们提出了一种新颖的架构，该结构通过将它们结合在联合重新骑行模块中，共同解决NER和EL任务。我们表明，我们提出的框架将NER的准确性提高了3.13％，而EL的精度则提高了F1分数高达3.6％。所使用的功能还导致其他自然语言理解任务（例如域分类和语义解析）的精度。
Named Entity Recognition (NER) and Entity Linking (EL) play an essential role in voice assistant interaction, but are challenging due to the special difficulties associated with spoken user queries. In this paper, we propose a novel architecture that jointly solves the NER and EL tasks by combining them in a joint reranking module. We show that our proposed framework improves NER accuracy by up to 3.13% and EL accuracy by up to 3.6% in F1 score. The features used also lead to better accuracies in other natural language understanding tasks, such as domain classification and semantic parsing.
