Amazon Technologies, Inc.

Trevor Andrew Morse

Rama Krishna Sandeep Pokkunuri

Yi Zhang

Roger Scott Jenke

Meghana Puvvadi

Matthew Lee

Shang-Wen Daniel Li

Code search is used to support various features. Code segments may be indexed using a code structure representation of the code segment. Code segments may be compared for similarity to identify a match with an input code segment using a comparison of logic trees generated for the input code segment and a stored code segment in an entry with a matching index value.

Discovering matching code segments according to index and comparative similarity

Techniques for creating and calibrating natural-language understanding (NLU) machine learning models are described. In certain embodiments, a training service tunes parameters of a function, taking the output from an NLU machine learning model as an input of the function, to calibrate the NLU machine learning model's output to optimize the interpretability of the resulting output, e.g., confidence score(s). Embodiments herein include generating, by the NLU machine learning model, an output based at least in part on an input (e.g., utterance) from a user, and applying a tuned, output modifying function to the output from the NLU machine learning model to generate a modified output. An inference may be generated based at least in part on the modified output.

Confidence calibration for natural-language understanding models that provides optimal interpretability

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Seattle, WA, United States of America

Trevor Andrew Morse