Leveraging vast and continually updated knowledge from the Internet has been considered an important ability for a dialogue system. Therefore, the dialogue query generation task is proposed for generating search queries from dialogue histories, which will be submitted to a search engine for retrieving relevant websites on the Internet. In this regard, previous efforts were devoted to collecting conversations with annotated queries and training a query producer (QP) via standard supervised learning. However, these studies still face the challenges of data scarcity and domain adaptation. To address these issues, in this paper, we propose a semi-supervised learning framework -- SemiDQG, to improve model performance with unlabeled conversations. Based on the observation that the search query is typically related to the topic of dialogue response, we train a response-augmented query producer (RA) to provide rich and effective training signals for QP. We first apply a similarity-based query selection strategy to select high-quality RA-generated pseudo queries, which are used to construct pseudo instances for training QP and RA. Then, we adopt the REINFORCE algorithm to further enhance QP, with RA-provided rewards as fine-grained training signals. Experimental results and in-depth analysis of three benchmarks show the effectiveness of our framework in cross-domain and low-resource scenarios. Particularly, SemiDQG significantly surpasses ChatGPT and competitive baselines. Our code is available at \url{https://github.com/DeepLearnXMU/SemiDQG}.

翻译：利用互联网上海量且持续更新的知识被认为是对话系统的一项重要能力。因此，提出了对话查询生成任务，旨在从对话历史中生成搜索查询，这些查询将被提交给搜索引擎以检索互联网上的相关网站。为此，先前的研究致力于收集带有标注查询的对话，并通过标准监督学习训练查询生成器（QP）。然而，这些研究仍面临数据稀缺和领域适应性的挑战。为解决这些问题，本文提出了一种半监督学习框架——SemiDQG，以利用未标注对话提升模型性能。基于搜索查询通常与对话回复主题相关的观察，我们训练了一个回复增强型查询生成器（RA），为QP提供丰富且有效的训练信号。首先，我们采用基于相似度的查询选择策略，筛选出RA生成的高质量伪查询，用于构建训练QP和RA的伪实例。然后，我们采用REINFORCE算法进一步提升QP，以RA提供的奖励作为细粒度训练信号。在三个基准上的实验结果和深度分析表明，我们的框架在跨域和低资源场景下具有有效性。特别是，SemiDQG显著超越了ChatGPT及其他有竞争力的基线方法。我们的代码开源地址为：\url{https://github.com/DeepLearnXMU/SemiDQG}。