基于知识迁移和双向异步序列的对话生成模型

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.

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

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.