A machine reading understanding model training method and device based on a joint loss function

Abstract

The invention provides a machine reading understanding model training method and device based on a joint loss function. Specifically, when machine reading understanding model training is carried out,a loss function composed of a maximum likelihood estimation function and a minimum risk training function is used as an evaluation index of a machine reading understanding model so as to guide adjustment of parameters of the machine reading understanding model. The idea of the minimum risk training function is to use the loss function to describe the difference degree between the answer output bythe model and the standard answer. The maximum likelihood estimation function, namely loss, tries to find a group of model parameters, so that the loss value of the machine reading understanding modelon the training set is minimum, and therefore, compared with the mode of singly utilizing the maximum likelihood estimation function, the model trained by the method provided by the invention can extract answers more accurately.

Description

technical field [0001] The present application relates to the technical field of natural language processing, in particular to a method and device for training a machine reading comprehension model based on a joint loss function. Background technique [0002] At present, deep learning has achieved fruitful results in the fields of image recognition and speech recognition. Machine Reading Comprehension (MRC) has become a new hotspot in the field of artificial intelligence research and application. Its main function is to read and understand a given article or Context to automatically give answers to relevant questions. [0003] With the development of machine reading comprehension technology, the task of reading comprehension is also constantly upgrading, from the early "cloze form" to "single document reading comprehension" based on Wikipedia, such as Stanford SQuAD (Stanford Question Answering Dataset, Stanford Question Answering Dataset) as the task of the dataset. And f...

AI Technical Summary

Problems solved by technology

Therefore, the above-mentioned method of using the likelihood function to maximize the probability of the observed training samples as the goal of the training phase will have the problem of inconsistency with the evaluation indicators used in the testing phase.

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