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Method and terminal device for generating reply content of dialogue robot

A dialogue robot and content technology, applied in instrumentation, computing, semantic analysis, etc., can solve problems such as low diversity and sentence semantic loss, and achieve the effects of reducing overestimation, improving decoding strategy and loss function, and good generalization ability

Active Publication Date: 2020-11-24
SHENZHEN GRADUATE SCHOOL TSINGHUA UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0007] In order to solve the problem of sentence semantic loss and low diversity caused by the reply generation of existing dialogue robots, the present invention provides a method for generating reply content of dialogue robots and a terminal device

Method used

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  • Method and terminal device for generating reply content of dialogue robot
  • Method and terminal device for generating reply content of dialogue robot
  • Method and terminal device for generating reply content of dialogue robot

Examples

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Embodiment 1

[0021] The embodiment of the present invention proposes a method for generating reply content of a dialogue robot for a dialogue system. First, collect the training dialogue samples as the neural network generation model, and prepare the data: obtain the dialogue text from the relevant dialogue platform, and perform data preprocessing, mainly including word segmentation, word frequency statistics, vocabulary construction, low-frequency word filtering, etc.; select a A neural network generation model based on the encoder-decoder structure is used as the basic network architecture; then, a word prediction network is introduced into the decoder of the selected neural network generation model, and the decoder is required to predict the current word in the target utterance in each step of decoding. Subsequences that have not yet been generated, and an additional loss function is added to the training process to optimize the word prediction network; in addition, the maximum entropy r...

Embodiment 2

[0069] In this embodiment, the existing English dialogue data set DailyDialog is used to divide the training set and the test set. There is no intersection between the training set and the test set. The model is trained on the training set, and the quality and quality of dialog generation are evaluated on the test set. diversity. DailyDialog is a multi-round dialogue dataset for daily chat scenes constructed by the publisher of the dataset by crawling spoken English dialogue websites. It contains dialogues in daily life, covers a lot of emotional information, and has many more natural dialogue patterns. Three dialogue generation models in the prior art were selected for comparison, as follows:

[0070] (1) Seq2Seq with attention mechanism (AttnSeq2Seq): The Seq2Seq model with attention mechanism has shown effectiveness in various natural language processing tasks.

[0071] (2) Hierarchical Encoder-Decoder (HRED): Since the multi-round dialogue history consists of a series of ...

Embodiment 3

[0078] Such as figure 2 As shown, it is a schematic diagram of a terminal device for generating a reply from a dialogue robot provided by an embodiment of the present invention. The terminal device for generating the reply of the dialogue robot in this embodiment includes: a processor, a memory, and a computer program stored in the memory and operable on the processor, such as a data processing program. When the processor executes the computer program, it implements the steps in the above embodiments of the method for generating reply content of each dialogue robot, for example figure 1 Steps S1-S6 are shown.

[0079] Exemplarily, the computer program may be divided into one or more units, and the one or more units are stored in the memory and executed by the processor to implement the present invention. The one or more units may be a series of computer program instruction segments capable of accomplishing specific functions, and the instruction segments are used to describ...

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Abstract

The present invention provides a method for generating reply content of a dialogue robot and a terminal device. The method includes: obtaining dialogue text and performing data preprocessing to obtain a training sample of a neural network generation model; selecting a neural network generation model based on an encoder-decoder structure; Introduce the word prediction network in the decoder and add a loss function to the word prediction network to modify the original negative log likelihood loss function; add the maximum entropy regularization term to the modified original negative log likelihood loss function to obtain the final loss function ; Perform model training on the neural network generation model to obtain optimal parameters; the trained neural network generation model receives user input and generates a corresponding reply. Good generalization ability: Encoder-decoder models are not limited to a specific structure and can be combined with any end-to-end model. While significantly improving the diversity of replies, it can also take into account the quality of replies, enabling users to have a better interactive experience.

Description

technical field [0001] The invention relates to the field of computer natural language processing, in particular to a method for generating reply content of a dialogue robot and a terminal device. Background technique [0002] The reply content generation of the dialogue system belongs to the field of computer natural language processing. The dialogue system can be used in many fields, such as shopping assistants in shopping malls, voice assistants on mobile phones, etc., such as Microsoft Xiaobing and Siri, which we are familiar with. Applications of dialogue systems, dialogue systems have a wide range of potential applications and attractive commercial value. Non-task-oriented dialogue systems usually focus on talking to people in an open field, providing reasonable responses and entertainment functions, and have played a role in many practical applications. Statistics show that in online shopping scenarios, nearly 80% of the words are chat messages, and the way to deal w...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F16/332G06F40/30G06F40/12
CPCG06F40/12G06F40/30
Inventor 杨余久王艺如杨芳
Owner SHENZHEN GRADUATE SCHOOL TSINGHUA UNIV
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