Dynamic reasoning network and method for multi-hop questions and answers

Abstract

The invention provides a dynamic reasoning network and method for multi-hop questions and answers, and the network comprises: a paragraph selector which receives paragraphs and questions, and selectssub-paragraphs related to answers of the questions from the paragraphs; the encoding module that is used for enhancing interaction between the problem and the sub-paragraphs by using collaborative attention, and calculating to obtain final vector representation of the sub-paragraphs and vector representation of the problem; the entity graph construction module that is used for constructing an entity graph; the dynamic reasoning module that is used for reasoning the entity graph, repeatedly reading texts to simulate the process of analyzing information by people and constructing a problem remodeling mechanism so as to repeatedly read problems and related important parts; and the answer prediction module that is connected with the encoding module, is connected with the dynamic reasoning module and is used for receiving the final vector representation of the sub-paragraphs and outputting to obtain four types of prediction. The network establishes a question remodeling mechanism, and the mechanism can repeatedly read questions to simulate the reading habits of people so as to improve the understanding and reasoning ability of the multi-hop reasoning question and answer model.

Description

technical field [0001] The invention belongs to the field of machine reading comprehension, and in particular relates to a dynamic reasoning network and method for multi-hop question answering. Background technique [0002] Machine reading comprehension is a task that obtains the correct answer to a given question by reasoning over a set of texts, while multi-hop reasoning question answering is a subtask of machine reading comprehension that aims to find the answer to a given question across multiple passages; Most current multi-hop reasoning question answering models usually obtain answers by visiting the question only once, so the model may not be able to obtain enough textual information. [0003] The multi-hop reasoning question answering model mainly has three research directions. The first one is based on the memory network, which uses the memory unit to combine the question with the information obtained in each round, and through continuous iterative reasoning after s...

AI Technical Summary

Problems solved by technology

[0004] So far, the above three models have achieved some success, but there are still some limitations; first, most existing models directly reason in all given passages without passage selection to remove interfering passages, which increases the the amount of data processed; second, after existing models encode each text into a representation vector, each text is always rarely accessed, whether it is a question or a paragraph

And the model may not be able to obtain enough information by accessing the text only once or twice, which leads to the underutilization of text information

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