Training method and device and identification device for intent recognition

Abstract

Provided are a training method and device and an identification device for intent recognition. The method includes: acquiring a corpus text vector corresponding to a corpus in a corpus text; constructing a joint loss function formula of a training model; acquiring training data; performing segmentation processing on the training data and then mapping to the corresponding corpus text vector, recorded as a training vector; using the training model to predict a training sample vector, calculating a loss function value of each training model according to a prediction result; calculating the jointloss function value based on the loss function value of each model, judging whether the joint loss function value is less than a set threshold, if yes, ending the training, if not, updating each modelparameter, and continuing iterative training. Therefore, the generalization capability of intent recognition can be improved as much as possible, and semantic ambiguity and fault tolerance problems can be solved.

Description

technical field [0001] The invention relates to the field of intelligent technology, in particular to a training method, training device and recognition device for intent recognition based on multi-task learning. Background technique [0002] "Intent recognition" refers to determining the intent category of a piece of information input by a user to express a query requirement. The current intention recognition technology is mainly used in search engines, human-computer dialogue systems, etc., and can be divided into template / thesaurus-based and supervised classification-based methods. The template / thesaurus-based method mines specific intentions from user historical input. Template / thesaurus, if the user input matches the words in the template / thesaurus of the corresponding category, the input is considered to belong to the intent category; the method based on supervised classification builds an intent classification model based on historical input data to predict user input...

AI Technical Summary

Problems solved by technology

[0003] ①. Generalization ability. Template-based methods are limited by template and lexicon coverage issues. Supervised classification-based methods are limited by training corpus data scale and data quality issues

[0004] ②. Problems of ambiguity, lack of semantics, and fault tolerance. Short texts often have problems such as incompleteness, lack of semantics, and input errors. For example, the user enters "Night Queen of the Playing Bear" and actually wants to search for "Midnight Harem of Teddy Bear" "

However, there are two problems with this method: first, the method obtains text vectors based on other tasks, and then splices them with the text vectors of the target task, and then trains the classifier of this task, and the errors of other tasks may affect this task. Negative impact; secondly, if other tasks are irrelevant to the current task, a large amount of irrelevant external information is introduced, which may cause disturbance to the classification results

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