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Predicting Entity Popularity to Improve Spoken Entity Recognition by Virtual Assistants

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.

preprint2020arXivOpen access
Christophe Van GyselManos TsagkiasErnest PusateriIlya Oparin
Computation and LanguageInformation Retrieval
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Open access4 authors2 topics
Imported metadata coverageMissing code, dataset, citation and institution fields are tracked without dominating the paper.Details
Citations: 0Reviews: 0Saves: 0Code: not linkedDataset: not linkedInstitutions: 0

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Christophe Van GyselManos TsagkiasErnest PusateriIlya Oparin

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