公开(公告)号：US11580968B1

公开(公告)日：2023-02-14

申请号：US16455165

申请日：2019-06-27

Applicant: Amazon Technologies, Inc.

Inventor： Arshit Gupta ,  Peng Zhang ,  Rashmi Gangadharaiah ,  Garima Lalwani ,  Roger Scott Jenke ,  Hassan Sawaf ,  Mona Diab ,  Katrin Kirchhoff ,  Adel A. Youssef ,  Kalpesh N. Sutaria

IPC: G10L15/08 ,  G10L15/16 ,  G10L15/22 ,  H04L67/02 ,  G10L15/18 ,  G10L15/02

Abstract: Techniques are described for a contextual natural language understanding (cNLU) framework that is able to incorporate contextual signals of variable history length to perform joint intent classification (IC) and slot labeling (SL) tasks. A user utterance provided by a user within a multi-turn chat dialog between the user and a conversational agent is received. The user utterance and contextual information associated with one or more previous turns of the multi-turn chat dialog is provided to a machine learning (ML) model. An intent classification and one or more slot labels for the user utterance are then obtained from the ML model. The cNLU framework described herein thus uses, in addition to a current utterance itself, various contextual signals as input to a model to generate IC and SL predictions for each utterance of a multi-turn chat dialog.

公开(公告)号：US11562735B1

公开(公告)日：2023-01-24

申请号：US16836130

申请日：2020-03-31

Applicant: Amazon Technologies, Inc.

Inventor： Arshit Gupta ,  Julian E. S. Salazar ,  Peng Zhang ,  Katrin Kirchhoff ,  Yi Zhang

IPC: G10L15/18 ,  G10L15/197 ,  G10L15/26

Abstract: A spoken language understanding (SLU) system may include an automatic speech recognizer (ASR), an audio feature extractor, an optional synchronizer and a language understanding module. The ASR may produce a first set of input data representing transcripts of utterances. The audio feature extractor may produce a second set of input data representing audio features of the utterances, in particular, non-transcript specific characteristics of the speaker in one or more portions the utterances. The two sets of input data may be provided for the language understanding module to predict intents and slot labels for the utterances. The SLU system may use the optional synchronizer to align the two sets of input data before providing them to the language understanding module.