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Showing posts by Young-Bum Kim

June 11, 2019

Young-Bum Kim

Natural-language-understanding system that includes both a generic model for a language and several locale-specific models improves accuracy by an average of 59% over individual locale-specific models.

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May 03, 2019

Young-Bum Kim

In the past year, we’ve introduced name-free skill interaction for Alexa, which allows customers to invoke skills without mentioning them by name. Y. B. Kim explains how we add new skills to the skill selection model without causing "catastrophic forgetting."

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December 17, 2018

Young-Bum Kim

Data representation schemes — such as "embeddings" — are a hot topic in machine learning. At this year's IEEE Spoken Language Technologies conference, Alexa scientists present a new representation scheme that cuts skill selection error rate by 40%.

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October 31, 2018

Young-Bum Kim

Amazon researcher Young-Bum Kim describes some new modifications to the machine learning model that selects the one skill out of thousands best suited to a particular customer request.

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September 04, 2018

Young-Bum Kim

Classifying "domains", or topics of conversation, is so central to natural-language understanding that some systems have modules that just look for out-of-domain utterances. Amazon researchers show that training out-of-domain classifiers together with domain recognizers improves their performance.

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June 08, 2018

Young-Bum Kim

Amazon science leader Young-Bum Kim describes the HypRank neural model, which performs the second step in the two-step process that Alexa uses to select the best skill to handle a given utterance.

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June 07, 2018

Young-Bum Kim

Amazon science leader Young-Bum Kim describes the Shortlister neural model, which performs the first step in the two-step process that Alexa uses to select the best skill to handle a given utterance.

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