RecQR: Using Recommendation Systems for Query Reformulation to correct unseen errors in spoken dialog systems
Pages 1019 - 1022
Abstract
As spoken dialog systems like Siri, Alexa and Google Assistant become widespread, it becomes apparent that relying solely on global, one-size-fits-all models of Automatic Speech Recognition (ASR), Natural Language Understanding (NLU) and Entity Resolution (ER), is inadequate for delivering a friction-less customer experience. To address this issue, Query Reformulation (QR) has emerged as a crucial technique for personalizing these systems and reducing customer friction. However, existing QR models, trained on personal rephrases in history face a critical drawback - they are unable to reformulate unseen queries to unseen targets. To alleviate this, we present RecQR, a novel system based on collaborative filters, designed to reformulate unseen defective requests to target requests that a customer may never have requested for in the past. RecQR anticipates a customer’s future requests and rewrites them using state of the art, large-scale, collaborative filtering and query reformulation models. Based on experiments we find that it reduces errors by nearly 40% (relative) on the reformulated utterances.
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Index Terms
- RecQR: Using Recommendation Systems for Query Reformulation to correct unseen errors in spoken dialog systems
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Published In
September 2023
1406 pages
ISBN:9798400702419
DOI:10.1145/3604915
Copyright © 2023 Owner/Author.
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- SIGCHI: ACM Special Interest Group on Computer-Human Interaction
- SIGecom: Special Interest Group on Economics and Computation
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 14 September 2023
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RecSys '23: Seventeenth ACM Conference on Recommender Systems
September 18 - 22, 2023
Singapore, Singapore
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Overall Acceptance Rate 254 of 1,295 submissions, 20%
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