Semantic comprehension training method and system

A technology of semantic understanding and semantic annotation, applied in semantic analysis, natural language data processing, speech analysis, etc., can solve problems such as creating wrong data samples

Active Publication Date: 2018-08-17
AISPEECH CO LTD
View PDF18 Cites 26 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

This method automatically creates annotations about the recognized text through the ASR align

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Semantic comprehension training method and system
  • Semantic comprehension training method and system
  • Semantic comprehension training method and system

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0019] In order to make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be described clearly and completely in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments It is a part of the embodiments of the present invention, not all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative work shall fall within the protection scope of the present invention.

[0020] In the following, the implementation of the application is introduced first, and then experimental data will be used to verify the difference between the solution of the application and the prior art and what beneficial effects can be achieved.

[0021] Please refer to figure 1 , Which shows a flowchart of an embodiment of the se...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses a semantic comprehension training method and system, and electronic equipment. The method comprises the following steps of carrying out semantic annotation on the correct textof user voice data to generate the correct text with the semantic annotation; inputting the user voice data into a voice identification system for identification so as to obtain an identification text; and randomly inputting the correct text, the correct text with the semantic annotation, and the identification text into a semantic comprehension system so as to carry out unsupervised adaptive training on the semantic comprehension system. In the invention, the correct text is annotated and the identification text does not need to be annotated; and through carrying out unsupervised adaptive learning on the correct text, the correct text with the semantic annotation and the identification text without annotation, the semantic comprehension system which is robust to a voice identification error can be trained and acquired.

Description

Technical field [0001] The invention belongs to the technical field of semantic understanding training, and in particular relates to a semantic understanding training method and system for an intelligent dialogue speech platform. Background technique [0002] The Semantic Understanding (SLU, Spoken Language Understanding) module is a key component of the Spoken Dialogue System (SDS), which parses the user's utterances into corresponding semantic concepts. For example, the utterance "Show me flights from Boston to New York" could be parsed as (fromloc.city name=Boston, toloc.city name=New York). Usually, SLU problems are considered as semantic understanding tasks. We also focus on semantic understanding in this invention. Given sufficient in-domain data and deep learning models (e.g., recurrent neural networks, bidirectional long and short memory networks), statistical methods have achieved high performance in semantic understanding tasks. [0003] In the process of realiz...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G10L15/06G10L15/18G10L15/26G06F17/21G06F17/27
CPCG10L15/063G10L15/1822G10L15/26G06F40/117G06F40/30
Inventor 俞凯朱苏
Owner AISPEECH CO LTD
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products