Posts in category: Amazon AWS

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

Many of today’s most useful AI systems are multilabel classifiers: they map input data into multiple categories at once. An object recognizer, for instance, might classify a given image as containing sky, sea, and boats but not desert or clouds.Read More

Alexa’s ability to respond to customer requests is largely the result of machine learning models trained on annotated data. The models are fed sample texts such as “Play the Prince song 1999” or “Play River by Joni Mitchell”. In each text, labels are attached to particular words — SongName for “1999” and “River”, for instance, and ArtistName for Prince and Joni Mitchell. By analyzing annotated data, the system learns to classify unannotated data on its own.Read More

As Alexa expands into new countries, she usually has to be trained on new languages. But sometimes, she has to be re-trained on languages she’s already learned. British English, American English, and Indian English, for instance, are different enough that for each of them, we trained a new machine learning model from scratch.Read More

n order to engage customers in longer, more productive conversations, Alexa needs to solve the problem of reference resolution. If Alexa says, “‘Believer’ is by Imagine Dragons”, for instance, and the customer replies, “Play their latest album”, Alexa should be able to deduce that “their” refers to Imagine Dragons.Read More

Today, customer exchanges with Alexa are generally either one-shot requests, like “Alexa, what’s the weather?”, or interactions that require multiple requests to complete more complex tasks.Read More