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Predicting Entity Popularity to Improve Spoken Entity Recognition by Virtual Assistants

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arxiv 2005.12816 v1 pith:M5OCGQST submitted 2020-05-26 cs.IR cs.CL

Predicting Entity Popularity to Improve Spoken Entity Recognition by Virtual Assistants

Christophe Van Gysel , Manos Tsagkias , Ernest Pusateri , Ilya Oparin This is my paper
classification cs.IR cs.CL
keywords entityrecognitionemergingentitiespopularityspokenvirtualapproach
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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.

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