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Multi-round dialogue reply generation system and method based on relational graph attention network

A technology of attention and relationship graph, applied in special data processing applications, instruments, electrical digital data processing and other directions, can solve the problem of less consideration of interlocutor's discourse dependency time sequence information, etc., and achieve the effect of high correlation

Pending Publication Date: 2022-04-05
HANGZHOU DIANZI UNIV
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Problems solved by technology

However, previous research work rarely considered the utterance dependencies between interlocutors and the timing information

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  • Multi-round dialogue reply generation system and method based on relational graph attention network
  • Multi-round dialogue reply generation system and method based on relational graph attention network
  • Multi-round dialogue reply generation system and method based on relational graph attention network

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Embodiment Construction

[0054] In order to better understand the purpose, structure and function of the present invention, a system and method for generating multi-round dialogue responses based on a relationship graph attention network of the present invention will be further described in detail below in conjunction with the accompanying drawings.

[0055] Such as figure 1 As shown, the multi-round dialogue reply generation method based on the relationship graph attention network of the present invention includes the following steps:

[0056] Step 1: Obtain the input content of multiple rounds of dialogues for preprocessing, and convert the semantic information of words in each round of utterances into corresponding vector representations through the pre-trained BERT model, so as to obtain the semantic information representation of each round of utterances, and then pass Bi-GRU The model encodes the semantic information of each round of utterance sentences, so as to obtain the semantic representatio...

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Abstract

The invention belongs to the field of computer artificial intelligence of natural language generation, and discloses a multi-round dialogue reply generation system and method based on a relational graph attention network. Comprising the steps of obtaining multi-round dialogue input content for preprocessing, obtaining semantic information representation of each round of utterance, and encoding statement semantic information of each round of utterance to obtain semantic representation of a dialogue context; then, a graph attention network is adopted to capture autocorrelation in multiple rounds of dialogues and related features among dialogues, and relation position codes are introduced into the graph attention network to illustrate sequence information containing utterances, so that high-level semantic representation of a graph coding layer is obtained; and finally, taking the context semantic information representation of the dialogue and the advanced semantic representation of the attention coding of the relational graph as input, and decoding by using a GRU model to generate a final dialogue reply output representation. According to the method, the generation quality of the multi-round dialogue reply is remarkably improved, and the generated reply is more coherent and meaningful.

Description

technical field [0001] The invention belongs to the field of computer artificial intelligence generated by natural language, and in particular relates to a multi-round dialogue reply generation system and method based on a relationship graph attention network. Background technique [0002] With the rapid rise of the Internet and the rapid development of social media, a large number of user dialogue corpora have emerged, which provides conditions for data-driven dialogue systems. The huge research and commercial value of intelligent dialogue systems has attracted more and more research attention from academia and industry. At present, dialogue systems can be divided into task-driven limited-domain dialogue systems and open-domain dialogue systems without specific tasks. Compared with the former, the latter has better practicability, scalability, and domain adaptability. Dialogue systems have gradually become a hot spot for researchers. [0003] At present, according to the ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/332
Inventor 林菲钱朝辉张聪
Owner HANGZHOU DIANZI UNIV
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