# Selecting Query-bag as Pseudo Relevance Feedback for Information-seeking Conversations

arXiv: https://arxiv.org/abs/2404.04272 · HTML: https://mlsanity.com/paper/2404.04272/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 22 references that are in the map.
- First posted on arXiv 22 Mar 2024.

## Builds on (most cited first, 15 of 15)

- [The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems](https://mlsanity.com/paper/1506.08909/) (2015)
- [Sequential Matching Network: A New Architecture for Multi-turn Response Selection in Retrieval-based Chatbots](https://mlsanity.com/paper/1612.01627/) (2016)
- [Query Resolution for Conversational Search with Limited Supervision](https://mlsanity.com/paper/2005.11723/) (2020)
- [Improving Query Representations for Dense Retrieval with Pseudo Relevance Feedback](https://mlsanity.com/paper/2108.13454/) (2021)
- [ISEEQ: Information Seeking Question Generation using Dynamic Meta-Information Retrieval and Knowledge Graphs](https://mlsanity.com/paper/2112.07622/) (2021)
- [Response Ranking with Deep Matching Networks and External Knowledge in Information-seeking Conversation Systems](https://mlsanity.com/paper/1805.00188/) (2018)
- [IART: Intent-aware Response Ranking with Transformers in Information-seeking Conversation Systems](https://mlsanity.com/paper/2002.00571/) (2020)
- [Learning to Respond with Stickers: A Framework of Unifying Multi-Modality in Multi-Turn Dialog](https://mlsanity.com/paper/2003.04679/) (2020)
- [QRFA: A Data-Driven Model of Information-Seeking Dialogues](https://mlsanity.com/paper/1812.10720/) (2018)
- [Exploring Dense Retrieval for Dialogue Response Selection](https://mlsanity.com/paper/2110.06612/) (2021)
- [TripleNet: Triple Attention Network for Multi-Turn Response Selection in Retrieval-based Chatbots](https://mlsanity.com/paper/1909.10666/) (2019)
- [Learning to Respond with Your Favorite Stickers: A Framework of Unifying Multi-Modality and User Preference in Multi-Turn Dialog](https://mlsanity.com/paper/2011.03322/) (2020)
- [Multi-Intent Attribute-Aware Text Matching in Searching](https://mlsanity.com/paper/2402.07788/) (2024)
- [Learning to Expand: Reinforced Pseudo-relevance Feedback Selection for Information-seeking Conversations](https://mlsanity.com/paper/2011.12771/) (2020)
- [Transfer Learning for Context-Aware Question Matching in Information-seeking Conversations in E-commerce](https://mlsanity.com/paper/1806.05434/) (2018)

## Abstract (arXiv)

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.