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Q&A method based on hierarchal memory network

A layered and memorized technology, applied in special data processing applications, instruments, electrical digital data processing, etc., can solve problems such as inability to select accurate user information, solve the problem of answer selection, reduce computational complexity, and avoid interfering information. Effect

Active Publication Date: 2016-11-16
INST OF AUTOMATION CHINESE ACAD OF SCI
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Problems solved by technology

[0010] Assuming that "Williamson", "577838771" and "0016178290851" are low-frequency words or unregistered words, if traditional methods discard these words or replace them with "unk" symbols, these methods cannot select accurate words from the dialogue text. User information for

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  • Q&A method based on hierarchal memory network
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[0029] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with specific embodiments and with reference to the accompanying drawings.

[0030] The invention discloses a question answering method based on a hierarchical memory network, which is based on an end-to-end model of a full neural network structure, which can realize information reasoning, screening and word granularity selection in a sentence set, and effectively solve question answering under big data The system selects answers to low-frequency words or unregistered words. In the question answering method of the present invention, sentence sets with time series information are respectively subjected to two hierarchical memory encodings, namely: sentence granularity memory encoding and word granularity memory encoding. Then information reasoning, screening and activation are performed based on th...

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Abstract

The invention provides a Q&A method based on a hierarchal memory network. The method comprises the following steps: firstly performing sentence granularity memory encoding, and finishing the information inference of the sentence granularity memory unit through an attention mechanism in multi-rounds of iterations under the stimulation of the problem semantic encoding, screening the sentence through k maximum sampling, and further performing the word granularity memory encoding on the basis of the sentence granularity encoding, namely, performing the memory encoding on two levels to form the hierarchal memory encoding; the output word probability distribution is predicted through the combination of the sentence granularity memory unit and the word granularity memory unit, the accuracy of the automatic Q&A is improved, and the answer selection problem of low-frequency word and unlisted word is effectively solved.

Description

technical field [0001] The present invention relates to the technical field of building an automatic question answering system, and more particularly relates to an end-to-end question answering method based on a hierarchical memory network. Background technique [0002] Automatic question answering has long been one of the most challenging tasks in natural language processing, which requires deep understanding of text and screening candidate answers as system responses. The existing traditional methods include: using the pipeline mode to independently train each module in the text processing process, and then merging the output mode; building a large-scale structured knowledge base, and based on this knowledge base for information reasoning and answer prediction. In recent years, end-to-end systems based on deep learning methods have been widely used to solve various tasks, these methods do not need to manually construct features, and do not require individual tuning of each...

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Application Information

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IPC IPC(8): G06F17/30G06F17/27
CPCG06F16/3329G06F40/30
Inventor 许家铭石晶姚轶群徐波
Owner INST OF AUTOMATION CHINESE ACAD OF SCI
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