Customer-obsessed science
Research areas
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August 21, 20269 min readExtendable framework enables testing agents on the full set of capabilities required to successfully complete a procedure, not isolated proxy tasks.
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July 30, 20268 min read
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July 9, 202610 min read
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Featured news
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SIGIR 20202020Even though Automatic Speech Recognition (ASR) systems significantly improved over the last decade, they still introduce a lot of errors when they transcribe voice to text. One of the most common reasons for these errors is phonetic confusion between similar-sounding expressions. As a result, ASR transcriptions often contain “quasi-oronyms", i.e., words or phrases that sound similar to the source ones,
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IJCAI 20202020We study the problem of fitting task-specific learning rate schedules from the perspective of hyper-parameter optimization, aiming at good generalization. We describe the structure of the gradient of a validation error w.r.t. the learning rate schedule – the hypergradient. Based on this, we introduce MARTHE, a novel online algorithm guided by cheap approximations of the hypergradient that uses past information
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IJCAI 2020, SIGIR 20202020Voice shopping using natural language introduces new challenges related to customer queries, like handling mispronounced, mis-expressed, and misunderstood queries. Voice null queries, which result in no offers, have negative impact on customers shopping experience. Query rewriting (QR) attempts to automatically replace null queries with alternatives that lead to relevant results. We present a new approach
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SIGIR 20202020Community question-answering (CQA) has been established as a prominent web service enabling users to post questions and get answers from the community. Product Question Answering (PQA) is a special CQA framework where questions are asked (and are answered) in the context of a specific product. Naturally, humorous questions are integral part of such platforms, especially as some products attract humor due
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KDD 20202020There is an influx of heterogeneous information network (HIN) based recommender systems in recent years since HIN is capable of characterizing complex graphs and contains rich semantics. Although the existing approaches have achieved performance improvement, while practical, they still face the following problems. On one hand, most existing HIN-based methods rely on explicit path reachability to leverage
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