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Showing posts tagged with Alexa Research

January 24, 2019

Alessandro Moschitti

New method for comparing data structures enables natural-language-understanding system to learn in four hours what used to take more than seven days.

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January 22, 2019

Anuj Goyal

A new approach leverages hundreds of millions of unannotated Alexa requests to improve the quality of "transfer learning", or adapting an existing neural network, trained on abundant data, to a new task for which training data is scarce.

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January 15, 2019

Anish Acharya

By compressing the huge lookup tables that list "embeddings", or vector representations of individual words, a new system can shrink neural-network models by up to 90%, with minimal effect on accuracy.

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December 21, 2018

Rasool Fakoor

Personal memory retrieval systems are usually trained to do one thing then judged on their ability to do something else. Alexa scientists show how reinforcement learning lets them use the same criterion (F1 score) to both train and evaluate such systems.

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December 18, 2018

Sanchit Agarwal

A new parser that learns which words of an utterance belong together enables Alexa to handle multistep requests.

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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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December 13, 2018

Ankur Gandhe

New Alexa capabilities are often bootstrapped using "grammars", formal rules that can generate artificial training examples for machine learning systems. A new method for constructing statistical language models directly from grammars can improve speech recognition on new capabilities by up to 15%.

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December 11, 2018

Chengwei Su

A new technique that lets different natural-language-understanding modules independently calibrate their judgments about whether they should handle a given utterance helps improvements reach Alexa customers more efficiently.

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December 07, 2018

Ruhi Sarikaya

In an invited talk at the second NeurIPS workshop on Conversational AI today, Alexa scientist Ruhi Sarikaya will focus on the role of context in redefining human-computer interaction through natural language.

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December 04, 2018

Behnam Hedayatnia

The addition of information about the last few turns of conversation and a small set of standard "dialogue act" tags boost Alexa's ability to determine the topic of an utterance by 35%.

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November 19, 2018

Trevor Wood

Alexa researchers show that their new neural-network-based text-to-speech system can learn how to read news in the style of a TV newscaster from just a few hours of training data.

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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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October 25, 2018

Larry Hardesty

At this year's Conference on Empirical Methods in Natural Language Processing (EMNLP), Amazon researchers are cohosting what they hope will be the first in a series of annual workshops that will both catalyze and publicize research on automatic fact verification.

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October 04, 2018

Jun Yang

Using an Echo's microphone array to perform sound-source localization could provide Alexa with useful information about a customer's physical context and enable the use of claps, taps, or snaps as control signals.

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October 02, 2018

Chieh-Chi Kao

Alexa scientists describe two different approaches to the problem of acoustic-event detection, the research topic that led to the new Alexa Guard home security feature. 

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