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Modeling Spoken Information Queries for Virtual Assistants: Open Problems, Challenges and Opportunities





Virtual assistants are becoming increasingly important speech-driven Information Retrieval platforms that assist users with various tasks. We discuss open problems and challenges with respect to modeling spoken information queries for virtual assistants, and list opportunities where Information Retrieval methods and research can be applied to improve the quality of virtual assistant speech recognition. We discuss how query domain classification, knowledge graphs and user interaction data, and query personalization can be helpful in improving the accurate recognition of spoken information…



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Conformer-Based Speech Recognition on Extreme Edge-Computing Devices

This paper was accepted at the Industry Track at NAACL 2024. With increasingly more powerful compute capabilities and resources in today’s devices, traditionally compute-intensive automatic speech recognition (ASR) has been moving from the cloud to devices to better protect...

AGRaME: Any Granularity Ranking with Multi-Vector Embeddings

Ranking is a fundamental and popular problem in search. However, existing ranking algorithms usually restrict the granularity of ranking to full passages or require a specific dense index for each desired level of granularity. Such lack of flexibility...

Time Sensitive Knowledge Editing through Efficient Finetuning

Large Language Models (LLMs) have demonstrated impressive capability in different tasks and are bringing transformative changes to many domains. However, keeping the knowledge in LLMs up-to-date remains a challenge once pretraining is complete. It is thus essential to...