面向人机对话意图分类的混合神经网络模型

Hybrid Neural Network Models for Human-machine Dialogue Intention Classification

随着人机对话的不断发展,让计算机能够准确地理解用户查询意图,对整个人机对话领域都有着重要意义.意图分类的主要目标是在人机对话的过程中判断用户的意图,提升人机对话系统的准确度与自然度.首先分析多个分类模型在意图分类任务上的优缺点.在此基础上,提出一种混合神经网络模型,综合利用多个深度网络模型的多样性输出.在输入特征预处理上,采用语言模型词向量,将语言模型拥有的语义挖掘能力应用到混合网络中,可以进一步提升模型的表达能力.所提出的混合神经网络模型相对于最好的基准模型在两份数据集上分别取得了2.95%和3.85%的性能提升.新模型在该数据上取得了最优的性能.

With the development of human-machine dialogue,it is of great significance for the computer to accurately understand the user’s query intention in human-machine dialogue systems.Intention classification aims at judging the user’s intention in human machine dialogue and improves the accuracy and naturalness of the human machine dialogue system.This study first analyzes the advantages and disadvantages of multiple classification models in the intention classification task.On this basis,this study proposes a hybrid neural network model to comprehensively utilize the diversity outputs of multiple deep network models.To further improve the perfoance,the language model embedding is used in the input feature preprocessing and the semantic mining ability possessed for the hybrid network which can effectively improve the expression ability of the model.The proposed model achieves 2.95%and 3.85%performance improvement on the two data sets respectively compared to the best benchmark model.The proposed model also achieves the top performance in a shared task.