Apple is sponsoring the forty-third Special Interest Group on Information Retrieval (SIGIR) Conference, which will be held virtually from July 25 to 30. SIGIR is a leading international forum focused on presenting new research in the informational retrieval field.

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Conference Accepted Paper

Predicting Entity Popularity to Improve Spoken Entity Recognition by Virtual Assistants

Christophe Van Gysel, Manos Tsagkias, Ernest Pusateri, Ilya Oparin

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 twenty percent relative reduction in errors on emerging entity name utterances without degrading the overall recognition quality of the system.

Related readings and updates.

Noise-robust Named Entity Understanding for Virtual Assistants

Named Entity Understanding (NEU) plays an essential role in interactions between users and voice assistants, since successfully identifying entities and correctly linking them to their standard forms is crucial to understanding the user's intent. NEU is a challenging task in voice assistants due to the ambiguous nature of natural language and because noise introduced by speech transcription and user errors occur frequently in spoken natural…
See paper details

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…
See paper details