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.

翻译：本文提出了一种面向知识密集型对话的无监督查询增强方法——QKConv。QKConv包含三个模块：查询生成器、现成的知识选择器和响应生成器。通过联合训练优化QKConv，该模型通过探索多个候选查询并利用对应选择的知识来生成响应。联合训练仅依赖对话上下文和目标响应，无需额外的查询标注或知识来源。为评估所提QKConv的有效性，我们在三个代表性知识密集型对话数据集上进行了实验：对话式问答、任务导向型对话和知识驱动型对话。实验结果表明，QKConv在三个数据集上的表现均优于所有无监督方法，并且与有监督方法相比具有竞争力的性能。