Selecting Query-bag as Pseudo Relevance Feedback for Information-seeking
  Conversations
Xiaoqing Zhang Xiuying Chen Shen Gao Shuqi Li Xin Gao Ji-Rong Wen Rui Yan
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.