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- W4386728844 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." @default.
- W4386728844 created "2023-09-15" @default.
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- W4386728844 date "2023-09-14" @default.
- W4386728844 modified "2023-09-27" @default.
- W4386728844 title "RecQR: Using Recommendation Systems for Query Reformulation to correct unseen errors in spoken dialog systems" @default.
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- W4386728844 doi "https://doi.org/10.1145/3604915.3610235" @default.
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