Transcription error resilient training of neural semantic parsers

Abstract

A method includes obtaining a set of training queries that each specify a corresponding operation to perform and include a corresponding plurality of speech recognition hypotheses that each represent a corresponding candidate transcription of the training query, and a corresponding ground-truth transcription of the training query. For each training query, the method includes processing, using an encoder of a neural semantic parsing (NSP) model, the corresponding plurality of speech recognition hypotheses to generate a corresponding NSP embedding, processing, using a transcription decoder, the corresponding NSP embedding to generate a corresponding predicted transcription, and determining a corresponding first loss based on the corresponding predicted transcription and the corresponding ground-truth transcription. The method further includes training, based on the first losses determined for the set of training queries, the NSP model to learn how to predict user intents associated with the operations specified by the training queries.