| CPC G10L 15/1815 (2013.01) [G06F 40/205 (2020.01); G06F 40/284 (2020.01); G06N 20/00 (2019.01)] | 22 Claims |
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1. A method, comprising:
processing an utterance using a trained machine learning model;
partially delexicalizing the utterance using the trained machine learning model by replacing a first portion of the utterance with a first token from a slot vocabulary, wherein the first token represents a semantic role of the first portion of the utterance, and wherein the slot vocabulary includes a plurality of words or phrases each associated with one or more tokens included in the slot vocabulary;
determining, using the trained machine learning model, a slot value in the processed utterance based on the first token in the partially delexicalized utterance;
delexicalizing, in at least one candidate of one or more candidates that include the processed utterance, at least one word tagged with a slot type corresponding to a high variability slot type;
removing the at least one word from the at least one candidate based on a determination that a slot entropy score for the at least one candidate is above a threshold to create an altered candidate set;
determining, based on a calculated inverse of an average of one or more slot entropy scores, a parsing confidence score for each of the one or more candidates including the processed utterance;
selecting the processed utterance from among the one or more candidates based on the parsing confidence score for the processed utterance; and
performing a task corresponding to the utterance based on the determined slot value and the selected processed utterance.
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