Machine Reading Comprehension Method for Classical Chinese Based on Multi-task Joint Training

Abstract

The invention provides a method for machine reading comprehension of classical Chinese based on multi-task joint training, comprising the following steps: establishing a machine reading comprehension model of classical Chinese based on multi-task joint training, and then performing multi-task joint training on classical Chinese; the machine reading comprehension model of classical Chinese includes: Context extraction module, multi-model coding module, two-way matching module, sentence sentence module and answer classification module; use the context extraction module to extract classical Chinese texts, and input the result to the multi-model coding module for processing; use the multi-model coding module to encode classical Chinese texts to obtain its Vectorized representation; use the gate mechanism to fuse them; input the obtained results into the two-way matching module and the sentence-sentence module for processing, and use the answer classification module to process the output results of the two-way matching module and the sentence-sentence module to obtain the final result. The present invention can accurately perform sentence segmentation processing for classical Chinese, and can take into account the simultaneous existence of ancient Chinese and modern Chinese, and more accurately process classical Chinese.

Description

technical field [0001] The invention specifically relates to a machine reading comprehension method for classical Chinese based on multi-task joint training. Background technique [0002] Machine reading comprehension is an important task in natural language processing. Through models and algorithms, machines can understand human language and answer corresponding questions. The single-choice task is a sub-task in the machine reading comprehension task. Given the context, questions and options, the best answer is selected from multiple options. The common framework for existing machine reading comprehension models on single-choice tasks is to encode the context, questions, and options to obtain their vectorized representations, then interact with the context, questions, and options, and finally pass the interaction results of the context, questions, and options Classification is performed and the option with the highest probability is selected as the result. [0003] Existi...

AI Technical Summary

Problems solved by technology

In addition, when using machine reading applications in classical Chinese single-choice topics, there are often modern texts or options in the form of topics in the reading comprehension of classical Chinese texts; existing technologies that only use classical Chinese text models cannot take both ancient texts and modern texts into consideration. At the same time, the vectorized representation obtained in the text encoding is of poor quality

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