论文标题
学习检索实体感知知识并使用以任务为导向的对话系统的复制机制产生响应
Learning to Retrieve Entity-Aware Knowledge and Generate Responses with Copy Mechanism for Task-Oriented Dialogue Systems
论文作者
论文摘要
带有非结构化知识访问的以任务为导向的对话建模，作为第9对话系统技术挑战（DSTC 9）的轨道1，请求构建一个系统以生成对话记录和知识访问的响应的系统。该挑战可以分为三个子任务，（1）寻求知识转向检测，（2）知识选择，以及（3）知识接地的响应产生。我们使用预训练的语言模型Electra和Roberta，作为我们不同子任务的基础编码器。对于子任务1和2，诸如域和实体之类的粗粒信息用于增强知识使用情况。对于子任务3，我们使用潜在变量来编码对话框历史记录和选择知识，并生成响应与复制机制结合使用。同时，对模型的最终输出进行了一些有用的后处理策略，以在生成任务中进一步使用知识。如发布的评估结果所示，我们提出的系统在客观指标下排名第二，在人类指标下排名第四。
Task-oriented conversational modeling with unstructured knowledge access, as track 1 of the 9th Dialogue System Technology Challenges (DSTC 9), requests to build a system to generate response given dialogue history and knowledge access. This challenge can be separated into three subtasks, (1) knowledge-seeking turn detection, (2) knowledge selection, and (3) knowledge-grounded response generation. We use pre-trained language models, ELECTRA and RoBERTa, as our base encoder for different subtasks. For subtask 1 and 2, the coarse-grained information like domain and entity are used to enhance knowledge usage. For subtask 3, we use a latent variable to encode dialog history and selected knowledge better and generate responses combined with copy mechanism. Meanwhile, some useful post-processing strategies are performed on the model's final output to make further knowledge usage in the generation task. As shown in released evaluation results, our proposed system ranks second under objective metrics and ranks fourth under human metrics.
