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A question and answer method based on multi-task learning

A multi-task learning and task technology, applied in the direction of instruments, biological neural network models, calculations, etc., can solve a lot of repetitive work, ignore task-related information and other problems, and achieve the effect of improving the overall level

Active Publication Date: 2019-06-14
CHONGQING UNIV OF POSTS & TELECOMM
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  • Description
  • Claims
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AI Technical Summary

Problems solved by technology

Existing methods solve these two tasks separately, requiring a lot of repetitive work, while ignoring the rich correlation information between tasks

Method used

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  • A question and answer method based on multi-task learning
  • A question and answer method based on multi-task learning

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Embodiment Construction

[0022] Embodiments of the present invention are described below through specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific implementation modes, and various modifications or changes can be made to the details in this specification based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the diagrams provided in the following embodiments are only schematically illustrating the basic concept of the present invention, and the following embodiments and the features in the embodiments can be combined with each other in the case of no conflict.

[0023] Wherein, the accompanying drawings are for illustrative purposes only, and represent only schematic diagrams, rather than physical drawings, and should...

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Abstract

The invention relates to a question and answer method based on multi-task learning, which belongs to the field of artificial intelligence and comprises the following steps: S1, configuring a task-specific siamese encoder for each task, and encoding a preprocessed sentence into a distributed vector representation; S2, using a shared representation learning layer to share advanced information amongdifferent tasks; S3, performing softmax layer classification specific to the task; as for question and answer pair in k task and tag of question and answer pair in k task, inputting thefinal feature representation form of the formula into a softmax layer which is specific to the task to carry out binary classification; and S4, multi-task learning: training a multi-task learning model to minimize a cross entropy loss function. According to the method, multi-view attention learnt from different angles is utilized, so that the tasks can interact to learn more comprehensive sentencerepresentation, the multi-view attention scheme can also effectively collect attention information from different representation view angles, and the overall level of representation learning is improved.

Description

technical field [0001] The invention belongs to the field of artificial intelligence, and relates to a question answering method based on multi-task learning (MTL), which uses attention learned from different angles to simultaneously process answer selection and knowledge base question answering tasks. Background technique [0002] Question answering is an important and challenging application of natural language processing. In recent years, the application of deep neural networks in question answering tasks has achieved many successes, but different question answering tasks are solved individually, and it is time-consuming and expensive to design and train various models for specific tasks. Recently, in many natural language processing tasks, multi-task learning has been widely studied to solve multiple related tasks simultaneously. Multi-task learning is widely used in the field of natural language processing, such as text classification, sequence labeling, text summariza...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/332G06N3/04
Inventor 李鹏华赵芬朱智勤袁宇鹏李小飞
Owner CHONGQING UNIV OF POSTS & TELECOMM
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