选择查询袋作为信息检索对话的伪相关反馈
Selecting Query-bag as Pseudo Relevance Feedback for Information-seeking Conversations
AI总结：
针对信息检索对话中候选回复检索与重排序问题，提出QB-PRF框架，通过无监督对比学习选择同义查询并以多维注意力融合查询袋，从而增强查询表示并提升匹配性能。
摘要：
信息检索对话系统广泛应用于电子商务系统中，其回答必须经过定制以适应在线系统的特定设置。给定用户查询后，信息检索对话系统首先检索回复候选子集，随后通过重排序从候选集中进一步选择最佳回复。当前方法主要仅依据当前查询检索回复候选，然而，引入相似问题可能带来更加多样化的内容，从而有可能优化表示并改进匹配过程。因此，本文提出一种基于查询袋的伪相关反馈框架（QB-PRF），该框架利用相关查询构建查询袋，将其作为伪信号来指导信息检索对话。具体而言，我们首先提出查询袋选择模块（QBS），该模块利用对比学习，并借助从预训练VAE学到的表示，以无监督方式训练同义查询的选择。其次，我们提出查询袋融合模块（QBF），该模块融合同义查询，通过多维注意力计算增强原始查询的语义表示。我们在BERT和GPT-2这两个具有竞争力的预训练主干模型上验证了QB-PRF框架的有效性。在两个基准数据集上的实验结果表明，我们的框架相比强基线取得了更优性能。
英文摘要：
Information-seeking dialogue systems are widely used in e-commerce systems, with answers that must be tailored to fit the specific settings of the online system. Given the user query, the information-seeking dialogue systems first retrieve a subset of response candidates, then further select the best response from the candidate set through re-ranking. Current methods mainly retrieve response candidates based solely on the current query, however, incorporating similar questions could introduce more diverse content, potentially refining the representation and improving the matching process. Hence, in this paper, we proposed a Query-bag based Pseudo Relevance Feedback framework (QB-PRF), which constructs a query-bag with related queries to serve as pseudo signals to guide information-seeking conversations. Concretely, we first propose a Query-bag Selection module (QBS), which utilizes contrastive learning to train the selection of synonymous queries in an unsupervised manner by leveraging the representations learned from pre-trained VAE. Secondly, we come up with a Query-bag Fusion module (QBF) that fuses synonymous queries to enhance the semantic representation of the original query through multidimensional attention computation. We verify the effectiveness of the QB-PRF framework on two competitive pretrained backbone models, including BERT and GPT-2. Experimental results on two benchmark datasets show that our framework achieves superior performance over strong baselines.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
