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Earlier this year, at Amazon’s re:MARS conference, Alexa head scientist Rohit Prasad unveiled Alexa Conversations, a new service that allows Alexa skill developers to more easily integrate conversational elements into their skills. The announcement is an indicator of the next stage in Alexa’s evolution: more-natural, dialogue-based engagements that enable Alexa to aggregate data and refine requests to better meet customer needs.

In recent years, the amount of textual information produced daily has increased exponentially. This information explosion has been accelerated by the ease with which data can be shared across the web. Most of the textual information is generated as free-form text, and only a small fraction is available in structured format (Wikidata, Freebase etc.) that can be processed and analyzed directly by machines.

Amazon scientists are continuously expanding Alexa’s natural-language-understanding (NLU) capabilities to make Alexa smarter, more useful, and more engaging.

Amazon Alexa currently has more than 40,000 third-party skills, which customers use to get information, perform tasks, play games, and more. To make it easier for customers to find and engage with skills, we are moving toward skill invocation that doesn’t require mentioning a skill by name (as highlighted in a recent post).

At the annual meeting of the North American chapter of the Association for Computational Linguistics in June, researchers at Amazon and the University of Sheffield released a new dataset that can be used to train machine-learning systems to determine the veracity of factual assertions online. The dataset is called FEVER, for fact extraction and verification.

To be as useful as possible to customers, Alexa should be able to make educated guesses about the meanings of ambiguous utterances. If, for instance, a customer says, “Alexa, play the song ‘Hello’”, Alexa should be able to infer from the customer’s listening history whether the song requested is the one by Adele or the one by Lionel Richie.

Alexa’s ability to act on spoken requests depends on statistical models that translate speech to text and text to actions. Historically, the models’ decisions were one-size-fits-all: the same utterance would produce the same action, regardless of context.

This year, we’ve started to explore ways to make it easier for customers to find and engage with Alexa skills.

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.

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.