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
https://github.com/DeepLearnXMU/SemiDQG.

提出了一种半监督学习框架 SemiDQG，通过使用未标注的对话数据来改进模型性能，使用类似度选择策略选取高质量伪查询进一步训练模型，并采用 REINFORCE 算法作为精细化训练信号，实验结果表明该框架在跨领域和资源有限场景中具有显著优势。