基于知识迁移和双向异步序列的对话生成模型

doi:10.3785/j.issn.1008-973X.2022.03.011

浙江大学学报(工学版)

2022, Vol. 56

Issue (3): 520-530 DOI: 10.3785/j.issn.1008-973X.2022.03.011

计算机与控制工程

基于知识迁移和双向异步序列的对话生成模型

王勇超1(

),曹钰2,杨玉辉1,许端清2,*(

)

1. 浙江大学信息技术中心，浙江杭州 310027
2. 浙江大学计算机科学与技术学院，浙江杭州 310027

Dialogue generation model based on knowledge transfer and two-direction asynchronous sequence

Yong-chao WANG1(

),Yu CAO2,Yu-hui YANG1,Duan-qing XU2,*(

)

1. Information Technology Center, Zhejiang University, Hangzhou 310027, China
2. College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China

全文: PDF(1025 KB) HTML

摘要：

针对端到端的对话生成模型普遍存在无意义安全回复和大量重复词汇的问题，和将外部知识引入对话系统的挑战，提出基于知识迁移和双向异步序列的对话生成模型.将知识库中的外部知识融合到对话生成模型并显式地生成在回复语句中；使用预训练的知识库问答模型获取输入语句的知识表达、候选知识表达以及关键字；搭建2个编码器?解码器结构，通过双向异步解码将关键字显式地生成在对话回复中；编、解码阶段均引入预训练模型的知识理解和知识表达能力，提升对话生成对知识信息的捕捉能力.提出重复检测惩罚机制，通过赋予惩罚权重的方式减少对话生成中的重复词汇.实验结果表明，所提模型在自动评估和人工评价指标上均优于已有的对话生成方法.

关键词： 对话生成; 知识实体; 知识库问答; 双向异步生成; 序列到序列模型

Abstract:

A dialogue generation model based on knowledge transfer and two-direction asynchronous sequence generation was proposed, aiming to the generally meaningless safe replies and the problem of a large number of repetitive words in view of the end-to-end dialogue generation models, and the challenge of introducing external knowledge into the dialogue system. The external knowledge in the knowledge base was fused into the dialogue generation model and explicitly generated in the reply sentences. A pre-trained model based on the question and answering of the knowledge base was used to obtain the knowledge expressions of the input sentences, the knowledge expressions of the candidate answers, and keywords. The keywords were then used in the reply. Two encoder-decoder structure models were proposed, and the keywords were generated explicitly in the dialogue reply by two-direction asynchronous generation. The knowledge expressions and understanding capabilities of the pre-trained model were introduced to capture knowledge information to dialog generation at the encoding and decoding stages. A repetitive detection-penalty mechanism was proposed to reduce the repeated words problem by giving weight to punish the repetitive words. Experimental results show that the model outperforms better than existing methods in both automatic evaluation and manual evaluation indicators.

Key words: dialogue generation knowledge entity knowledge base question and answer two-direction asynchronous generation sequence-to-sequence model

收稿日期: 2021-07-11 出版日期: 2022-03-29

CLC:

TP 391

基金资助: 国家重点研发计划资助项目（2020YFC1523101, 2019YFC1521304）；浙江省重点研发计划资助项目（2021C03140）；宁波市2021科技创新重大专项（20211ZDYF020028）

通讯作者: 许端清 E-mail: ychwang@zju.edu.cn;xdq@cs.zju.edu.cn

作者简介: 王勇超（1975—），男，高级工程师，从事计算机视觉与自然语言处理研究. orcid.org/0000-0001-6394-4701. E-mail： ychwang@zju.edu.cn

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引用本文:

王勇超,曹钰,杨玉辉,许端清. 基于知识迁移和双向异步序列的对话生成模型[J]. 浙江大学学报(工学版), 2022, 56(3): 520-530.

Yong-chao WANG,Yu CAO,Yu-hui YANG,Duan-qing XU. Dialogue generation model based on knowledge transfer and two-direction asynchronous sequence. Journal of ZheJiang University (Engineering Science), 2022, 56(3): 520-530.

链接本文:

https://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2022.03.011 或 https://www.zjujournals.com/eng/CN/Y2022/V56/I3/520

图 1 基于知识迁移和双向异步序列的对话生成模型总体架构图

表 1 文本匹配不同粒度的三元组示例

图 2 BiLSTM网络中单一神经元结构图

图 3 BiLSTM网络结构图

表 2 本研究所用数据集详情

表 3 评价对话生成模型的人工评价标准

表 4 不同模型的自动评估结果统计

表 5 不同模型的人工评价结果统计

表 6 不同模型生成回复的实例对比

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