Posts in category: Amazon AWS

An automatic-speech-recognition system — such as Alexa’s — converts speech into text, and one of its key components is its language model. Given a sequence of words, the language model computes the probability that any given word is the next one. For instance, a language model would predict that a sentence that begins “Toni Morrison won the Nobel” is more likely to conclude “Prize” than “dries”. Language models can thus help decide between competing interpretations of the same acoustic information.Read More

A text-to-speech system, which converts written text into synthesized speech, is what allows Alexa to respond verbally to requests or commands…Read More

Embedding entity names from diverse skills in a shared representations space enables system to suggest neglected entity names with 88.5% accuracy.Read More

Neural networks are responsible for most recent advances in artificial intelligence, including many of Alexa’s latest capabilities. But neural networks tend to be large and unwieldy, and in recent years, the Alexa team has been investigating techniques for making them efficient enough to run on-device.Read More

Alexa currently has more than 90,000 skills, or abilities contributed by third-party developers — the Uber ride-sharing skill, the Jeopardy! trivia game skill, the Starbucks drink-ordering skill, and so on.Read More

This year, at the Association for Computational Linguistics’ Workshop on Natural-Language Processing for Conversational AI, my colleagues and I won one of two best-paper awards for our work on slot carryover.Read More

Computerized question-answering systems usually take one of two approaches. Either they do a text search and try to infer the semantic relationships between entities named in the text, or they explore a hand-curated knowledge graph, a data structure that directly encodes relationships among entities.Read More

This month, Amazon announced the 11 focus areas of the 2019 Amazon Research Awards.Read More

Using machine learning to train information retrieval models — such as Internet search engines — is difficult because it requires so much manually annotated data. Of course, training most machine learning systems requires manually annotated data, but because information retrieval models must handle such a wide variety of queries, they require a lot of data. Consequently, most information retrieval systems rely primarily on mechanisms other than machine learning.Read More

Earlier this month, Varun Sharma and Akshit Tyagi, two master’s students from the University of Massachusetts Amherst, began summer internships at Amazon, where, like many other scientists in training, they will be working on Alexa’s spoken-language-understanding systems.Read More