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Community question and answer system and method based on multi-task learning and electronic device

A multi-task learning, community question answering technology, applied in the field of intelligent question answering system, can solve problems such as lack of focus on question answering data set, poor answer selection model, single attention model, etc.

Active Publication Date: 2019-04-02
SHENZHEN INST OF ADVANCED TECH
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Problems solved by technology

Therefore, topic-agnostic answer selection models do not work well for legal forum answer selection
[0006] 2) Existing models often use a single attention model to capture important parts of the input
[0007] 3) There is currently no focus on legal-related question answering datasets

Method used

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  • Community question and answer system and method based on multi-task learning and electronic device

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Embodiment Construction

[0074] In order to make the purpose, technical solution and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present application, not to limit the present application.

[0075] Aiming at the problems existing in the prior art, this application designs a community question answering system (Community Question Answering, CQA) based on multi-task learning, using two tasks of related questions and related answers to carry out model training respectively, and between questions and answers Both use a multi-dimensional attention mechanism to optimize the model effect, and can select high-quality answers from forum answers according to the questions input by users, thereby improving the efficiency of user search. Specifically, see figure 1 , is a schematic struct...

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Abstract

The invention belongs to the technical field of internet databases, and particularly relates to a community question and answer system and method based on multi-task learning and an electronic device.The system comprises an answer selection model training module which is used for inputting answer input and question input into a bidirectional long-short memory network for encoding, inputting the encoded answer input and question input into a multi-dimensional attention layer, flattening and connecting an output result, and calculating the loss of a prediction result and a real result; a question classification model training module which is used for inputting questions into a bidirectional long and short memory network for encoding, inputting the encoded questions into a two-layer full connection network, and calculating the loss of a prediction result and a real result through a softmax layer; and a joint training module which is used for unifying the answer selection task and the question text classification task under a loss function for joint training to obtain an answer related to the input question. According to the application, the accuracy of the forum community question and answer system can be improved, and the search efficiency of the user is improved.

Description

technical field [0001] The application belongs to the technical field of intelligent question answering systems, and in particular relates to a community question answering system, method and electronic equipment based on multi-task learning. Background technique [0002] The legal forum is an online forum that provides free professional legal advice to individuals, and has attracted widespread attention as a new way to obtain legal advice. However, for a question, there are likely hundreds, if not thousands, of relevant answers in legal forums. However, in terms of answer quality and relevance, most of the answers are not what users need, and browsing these answers will consume a lot of time. [0003] In the prior art, there are 7 [Lei Yu, Karl Moritz Hermann, Phil Blunsom, and Stephen Pulman.2014. deep learning for answer selection. InProceedings of Deep Learning and Representation Learning Workshop.NIPS.], 8 [Cicero Dos Santos,Luciano Barbosa,DashaBogdanova,and BiancaZa...

Claims

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Application Information

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IPC IPC(8): G06F16/332G06Q50/00
CPCG06Q50/01
Inventor 曲强杨敏
Owner SHENZHEN INST OF ADVANCED TECH
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