基于实体知识推理的端到端任务型对话
End-to-End Task-Oriented Dialogue with Entity-Based Knowledge Inference

端到端任务型对话系统通过人机对话的方式来完成特定的任务以满足人类的需求，为了促进响应生成和实体预测，提出了一种基于实体知识推理的端到端任务型对话系统。该系统利用标记着对话历史实体信息的实体掩码，从对话历史编码中提取实体信息，并根据对话上下文推理出相关的实体信息以丰富当前上下文的实体知识；同时利用候选实体的正负样本与响应生成器的交叉注意力结合进行对比训练，将响应生成模型中的实体预测信号直接反馈给编码器以增强监督学习的能力；最终通过联合学习提高了模型的响应生成和实体预测能力。实验结果表明，在SMD、Multi-WOZ 2.1和CamRest数据集上，提出的模型能够生成丰富的响应内容，有着精准的实体预测准确率，并且验证了模型的有效性与实用性。
End-to-end task-oriented dialogue systems facilitate the completion of specific tasks through human-computer dialogue to meet human needs. To enhance response generation and entity prediction, we propose an end-to-end task-oriented dialogue system based on entity knowledge inference. This system leverages entity masks, annotated with dialogue history entity information, to extract entity information from the dialogue history encoding. It then infers relevant entity information from the dialogue context to enrich the current context’s entity knowledge. Simultaneously, the system employs contrastive training by integrating positive and negative samples of candidate entities with the cross-attention of the response generator. This setup directly feeds back entity prediction signals from the response generation model to the encoder, thereby enhancing the capability of supervised learning. Ultimately, the model’s response generation and entity prediction abilities are improved through joint learning. Experimental results indicate that on the SMD, Multi-WOZ 2.1, and CamRest datasets, the proposed model can generate rich response content with precise entity prediction accuracy, validating the model’s effectiveness and practicality.