Apple sponsored the forty-third Special Interest Group on Information Retrieval (SIGIR) Conference, which was held virtually from July 25 to 30. SIGIR is a leading international forum focused on presenting new research in the informational retrieval field.
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.
International ACM Conference on Research and Development in Information Retrieval (SIGIR) 2023
Apple sponsored the International ACM Conference on Research and Development in Information Retrieval (SIGIR), which took place in a hybrid virtual and in-person format from July 23 to 27 in Taipei, Taiwan. SIGIR is an international forum focused on presenting new research in the information retrieval field. See below what was the accepted paper presentation schedule at SIGIR 2023.
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
Discover opportunities in Machine Learning.
Our research in machine learning breaks new ground every day.