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Research on Modeling and Analysis of Generative Conversational System Based on Optimal Joint Structural and Linguistic Model

机译:基于最优联合结构和语言模型的生成对话系统建模与分析研究

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摘要

Generative conversational systems consisting of a neural network-based structural model and a linguistic model have always been considered to be an attractive area. However, conversational systems tend to generate single-turn responses with a lack of diversity and informativeness. For this reason, the conversational system method is further developed by modeling and analyzing the joint structural and linguistic model, as presented in the paper. Firstly, we establish a novel dual-encoder structural model based on the new Convolutional Neural Network architecture and strengthened attention with intention. It is able to effectively extract the features of variable-length sequences and then mine their deep semantic information. Secondly, a linguistic model combining the maximum mutual information with the foolish punishment mechanism is proposed. Thirdly, the conversational system for the joint structural and linguistic model is observed and discussed. Then, to validate the effectiveness of the proposed method, some different models are tested, evaluated and compared with respect to Response Coherence, Response Diversity, Length of Conversation and Human Evaluation. As these comparative results show, the proposed method is able to effectively improve the response quality of the generative conversational system.
机译:由神经网络的结构模型和语言模型组成的生成对话系统一直被认为是一个有吸引力的地区。然而,会话系统倾向于产生缺乏多样性和信息性的单反响应。因此,通过建模和分析纸张中提出的联合结构和语言模型进一步开发了会话系统方法。首先,我们基于新的卷积神经网络架构建立了一种新型双编码器结构模型,并强化意图的关注。它能够有效地提取可变长度序列的特征,然后挖掘它们的深度语义信息。其次,提出了一种与愚蠢惩罚机制相结合的语言模型。第三,观察并讨论了联合结构和语言模型的会话系统。然后,为了验证所提出的方法的有效性,与响应一致性,响应多样性,谈话长度和人体评估的响应相干性,响应分集和人类评估进行了测试,评估和比较了一些不同的模型。作为这些比较结果表明,该方法能够有效地改善生成的会话系统的响应质量。

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