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Showing posts tagged with Alexa Science

September 17, 2019

Dilek Hakkani-Tur

Data set includes more than 230,000 dialogue turns, most of which are annotated to indicate the sources of their factual assertions.

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September 16, 2019

Shuyang Gao

Treating a conversation as a text, and dialogue state tracking as answering questions about the text, enables an 11.75% improvement in accuracy over the best-performing prior system.

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September 10, 2019

Larry Hardesty

Research spans the five core areas of Alexa functionality, as well as more-general questions in machine learning.

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September 05, 2019

Pranav Ladkat

By combining two state-of-the-art techniques for parallelizing machine learning — one that prioritizes accuracy, one that prioritizes efficiency — Alexa researchers improve on both.

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September 04, 2019

Dilek Hakkani-Tur

Universal dialogue-act tagging scheme, hybrid slot-tracking system promise to improve dialogue state tracking.

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August 29, 2019

Anirudh Raju

Techniques include weighting training samples from out-of-domain data sets and noise contrastive estimation, which turns the calculation of massive probability distributions into simple binary decisions.

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August 22, 2019

Jaime Lorenzo Trueba

Two Interspeech papers report a system that transfers prosody — inflection and rhythm — from a recorded speaker to a synthesized voice and a neural vocoder that works with any speaker.

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August 15, 2019

Boya Yu

Based on embeddings, system suggests named entities — or "slot values" — that developers might want their skills to recognize.

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August 13, 2019

Chieh-Chi Kao

Two new papers explore techniques for increasing the computational efficiency and reducing the memory footprints of neural networks that process audio data.

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August 08, 2019

Mengwen Liu

Pooling the training data for related skills, and using it to train the skills simultaneously, improves performance for all of them.

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August 07, 2019

Chetan Naik

Different Alexa services use different names for the same types of data, which makes it hard to track references across dialogues. By learning correlations between data types, a machine learning model can make better decisions about which references to track from one round of dialogue to the next.

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July 31, 2019

Abdalghani Abujabal

Most question-answering systems rely on either text search or knowledge graphs. A hybrid approach, which knits together data from multiple textual sources to produce an ad hoc knowledge graph, yields better results on complex questions.

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July 29, 2019

Larry Hardesty

The new focus areas for the Amazon Research Awards, which provide up to $80,000 in funding and up to $20,000 in Amazon Web Services Promotional Credits to academic researchers investigating topics related to machine learning, were announced this month. The application period opens on September 10.

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July 22, 2019

Kai Hui

Treating news headlines and Wikipedia section headers as "search terms" and the associated texts as search results enables the training of neural search engines with less need for manually annotated data.

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June 27, 2019

Larry Hardesty

Student system demonstrates how "early exit" strategies could improve Alexa's efficiency, by letting neural networks break off computations when they have high confidence in their solutions.

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