Response Enhanced Semi-supervised Dialogue Query Generation

• URL: http://arxiv.org/abs/2312.12713v2
• Date: Fri, 16 Feb 2024 02:19:39 GMT
• Title: Response Enhanced Semi-supervised Dialogue Query Generation
• Authors: Jianheng Huang, Ante Wang, Linfeng Gao, Linfeng Song, Jinsong Su
• Abstract summary: We propose a semi-supervised learning framework -- SemiDQG -- to improve model performance with unlabeled conversations. We first apply a similarity-based query selection strategy to select high-quality RA-generated pseudo queries. We adopt the REINFORCE algorithm to further enhance QP, with RA-provided rewards as fine-grained training signals.
• Score: 40.17161986495854
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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}.

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