Google Inc.

Manaal Faruqui

Dipanjan Das

Tara N Sainath

Bo Li

Trevor D Strohman

Shuo-Yiin Chang

Qiao Liang

Guru Prakash Arumugam

Zelin Wu

Adam Stambler

Shyam Upadhyay

Siddharth Gopal

Janara Christensen

A method includes receiving, as input to a speech recognition model, audio data corresponding to a spoken utterance. The method also includes performing, using the speech recognition model, speech recognition on the audio data by, at each of a plurality of time steps, encoding, using an audio encoder, the audio data corresponding to the spoken utterance into a corresponding audio encoding, and decoding, using a speech recognition joint network, the corresponding audio encoding into a probability distribution over possible output labels. At each of the plurality of time steps, the method also includes determining, using an intended query (IQ) joint network configured to receive a label history representation associated with a sequence of non-blank symbols output by a final softmax layer, an intended query decision indicating whether or not the spoken utterance includes a query intended for a digital assistant.

Intended query detection using E2E modeling for continued conversation

Techniques are disclosed that enable clarifying whether a user query corresponds to a candidate intent when an intent score (indicating the probability the user query corresponds to the candidate intent) fails to satisfy a threshold likelihood value but is 'close' to satisfying the threshold likelihood value. For example, the intent score can fail to satisfy the threshold likelihood value but can satisfy an additional threshold likelihood value. Various implementations include generating the candidate intent and corresponding intent score by processing a natural language user query using a natural language understanding (NLU) model.

Natural language understanding clarifications

Techniques are described herein for training and/or utilizing a query canonicalization system. In various implementations, a query canonicalization system can include a classification model and a canonicalization model. A classification model can be used to determine if a search query is well-formed. Additionally or alternatively, a canonicalization model can be used to determine a well-formed variant of a search query in response to determining a search query is not well-formed. In various implementations, a canonicalization model portion of a query canonicalization system can be a sequence to sequence model.

Canonicalizing search queries to natural language questions

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Brooklyn, NY, United States of America

Manaal Faruqui