基于回复生成的对话意图预测

随着人机对话系统的不断发展,让计算机能够准确理解对话者的对话意图,并根据对话的历史信息对回复进行意图预测,对于人机对话系统有着十分重要的意义。已有研究重点关注根据对话文本和已有标签对回复进行意图预测,但是,在很多场景下回复可能并没有生成。因此,文中提出了一种结合回复生成的对话意图预测模型。在生成部分,使用Seq2Seq结构,根据对话历史信息生成文本,作为对话中未来回复的文本信息;在分类部分,利用LSTM模型,将生成的回复文本与已有的对话信息转变为子句级别的表示,并结合注意力机制突出同一轮次对话句与生成回复的联系。实验结果表明,所提出的模型相比简单基线模型取得了2.54%的F1-score提升,并且联合训练的方式有助于提升模型性能。

Dialogue Act Prediction Based on Response Generation

With the continuous development of the human-machine dialogue system,it is of great significance for the computer to accurately understand the speaker’s dialogue act and predict the act of response according to the history information of the dialogue.Previous research work focus on act prediction of responses based on dialogue text and existing labels.But in many scena-rios,the reply has not been generated.Therefore,this paper proposes a dialogue act prediction model based on reply generation.In the generation part,the Seq2Seq structure is used to generate text based on the conversation history information as text information for future replies in the conversation;in the classification part,the LSTM model is used to express the generated reply text and the existing conversation information as clause level representations.Combined with the attention mechanism,it highlights the connection between the dialogue sentence of the same round and the generated response.The experimental results show that the proposed model a chieves a 2.54% F1-score improvement compared to the simple baseline model,and the joint training method contributes to the improvement of model performance.