论文标题
查询通过无监督的联合建模增强的知识密集型对话
Query Enhanced Knowledge-Intensive Conversation via Unsupervised Joint Modeling
论文作者
论文摘要
在本文中，我们提出了一种无监督的查询来进行知识密集型对话的方法，即QKCONV。 QKCONV中有三个模块：一个查询生成器，一个现成的知识选择器和一个响应生成器。 QKCONV通过联合培训进行了优化，该联合培训通过探索多个候选查询并利用相应的选定知识来产生响应。联合培训仅依靠对话环境和目标响应，从而免于额外的查询注释或知识证明。为了评估拟议的QKCONV的有效性，我们对三个代表性知识密集的对话数据集进行了实验：对话询问，以任务为导向的对话和知识接触的对话。实验结果表明，与监督方法相比，QKCONV的性能优于三个数据集中的所有无监督方法，并且可以达到竞争性能。
In this paper, we propose an unsupervised query enhanced approach for knowledge-intensive conversations, namely QKConv. There are three modules in QKConv: a query generator, an off-the-shelf knowledge selector, and a response generator. QKConv is optimized through joint training, which produces the response by exploring multiple candidate queries and leveraging corresponding selected knowledge. The joint training solely relies on the dialogue context and target response, getting exempt from extra query annotations or knowledge provenances. To evaluate the effectiveness of the proposed QKConv, we conduct experiments on three representative knowledge-intensive conversation datasets: conversational question-answering, task-oriented dialogue, and knowledge-grounded conversation. Experimental results reveal that QKConv performs better than all unsupervised methods across three datasets and achieves competitive performance compared to supervised methods.
