Cross-domain language model training method and device, electronic equipment and storage medium

Abstract

The invention provides a cross-domain language model training method and device, electronic equipment and a storage medium. The method comprises the steps of obtaining corpus training sets of multipledomains; training a plurality of language models based on the corpus training sets of the plurality of fields to obtain respective outputs of the plurality of language models, the plurality of language models being in one-to-one correspondence with the corpus training sets of the plurality of fields; and according to respective outputs of the plurality of language models and preset interpolationcoefficients of the plurality of language models, interpolating the plurality of language models to obtain a cross-domain language model. According to the cross-domain language model training method provided by the invention, language models in multiple domains are mixed into one model through a language model mixing method based on linear interpolation, so that the cross-domain vulnerability of the language model is effectively solved, the performance index of the language model is improved, and the confusion degree of the language model is reduced.

Description

technical field [0001] The present invention relates to the technical field of natural language processing, in particular to a cross-domain language model training method, device, electronic equipment and storage medium. Background technique [0002] Natural language processing (NLP) is the intersection of computer science, linguistics and machine learning. It is dedicated to enabling computers to understand and generate human language. However, computers cannot directly perform mathematical operations on language characters, and need to convert text into mathematical symbols. Then apply various algorithms for processing to get the results we want. The language model is an abstract mathematical modeling of language based on the objective facts of language, and converts natural language text into mathematical symbols with semantic features. Therefore, the performance of the language model directly affects the performance of the NLP system. [0003] Although the theoretical f...

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Problems solved by technology

[0003] Although the theoretical basis of the language model is relatively complete, the language model often encounters some difficult problems in practical applications, and the model's vulnerability to cross domains (brittleness across domains) is the most significant problem.

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