# Response Ranking with Deep Matching Networks and External Knowledge in Information-seeking Conversation Systems

arXiv: https://arxiv.org/abs/1805.00188 · HTML: https://mlsanity.com/paper/1805.00188/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 May 2018.

## Built on by (most cited first, 18 of 18)

- [On the Calibration and Uncertainty of Neural Learning to Rank Models](https://mlsanity.com/paper/2101.04356/) (2021)
- [A Survey on Recent Advances in Conversational Data Generation](https://mlsanity.com/paper/2405.13003/) (2024)
- [Crossing the Conversational Chasm: A Primer on Natural Language Processing for Multilingual Task-Oriented Dialogue Systems](https://mlsanity.com/paper/2104.08570/) (2021)
- [Dialogue History Matters! Personalized Response Selectionin Multi-turn Retrieval-based Chatbots](https://mlsanity.com/paper/2103.09534/) (2021)
- [Analysing Mixed Initiatives and Search Strategies during Conversational Search](https://mlsanity.com/paper/2109.05955/) (2021)
- [RE-Matching: A Fine-Grained Semantic Matching Method for Zero-Shot Relation Extraction](https://mlsanity.com/paper/2306.04954/) (2023)
- [You Truly Understand What I Need: Intellectual and Friendly Dialogue Agents grounding Knowledge and Persona](https://mlsanity.com/paper/2301.02401/) (2023)
- [Meta-evaluation of Conversational Search Evaluation Metrics](https://mlsanity.com/paper/2104.13453/) (2021)
- [Weakly-Supervised Open-Retrieval Conversational Question Answering](https://mlsanity.com/paper/2103.02537/) (2021)
- [FCC: Fusing Conversation History and Candidate Provenance for Contextual Response Ranking in Dialogue Systems](https://mlsanity.com/paper/2304.00180/) (2023)
- [Unstructured Text Enhanced Open-domain Dialogue System: A Systematic Survey](https://mlsanity.com/paper/2411.09166/) (2024)
- [Sparse and Dense Approaches for the Full-rank Retrieval of Responses for Dialogues](https://mlsanity.com/paper/2204.10558/) (2022)
- [Evaluating the Robustness of Conversational Recommender Systems by Adversarial Examples](https://mlsanity.com/paper/2303.05575/) (2023)
- [A Joint-Reasoning based Disease Q&A System](https://mlsanity.com/paper/2401.03181/) (2024)
- [Selecting Query-bag as Pseudo Relevance Feedback for Information-seeking Conversations](https://mlsanity.com/paper/2404.04272/) (2024)
- [A Stack-Propagation Framework for Low-Resource Personalized Dialogue Generation](https://mlsanity.com/paper/2410.20174/) (2024)
- [Structure-Aware RAG: Structured Retrieval Augmented Generation from Noisy Data for Conversational Agents](https://mlsanity.com/paper/2605.24366/) (2026)
- [RAFT: A Stateful Retrieval-Augmented Framework for Troubleshooting Agents](https://mlsanity.com/paper/2609.20754/) (2026)

## Abstract (arXiv)

Intelligent personal assistant systems with either text-based or voice-based conversational interfaces are becoming increasingly popular around the world. Retrieval-based conversation models have the advantages of returning fluent and informative responses. Most existing studies in this area are on open domain "chit-chat" conversations or task / transaction oriented conversations. More research is needed for information-seeking conversations. There is also a lack of modeling external knowledge beyond the dialog utterances among current conversational models. In this paper, we propose a learning framework on the top of deep neural matching networks that leverages external knowledge for response ranking in information-seeking conversation systems. We incorporate external knowledge into deep neural models with pseudo-relevance feedback and QA correspondence knowledge distillation. Extensive experiments with three information-seeking conversation data sets including both open benchmarks and commercial data show that, our methods outperform various baseline methods including several deep text matching models and the state-of-the-art method on response selection in multi-turn conversations. We also perform analysis over different response types, model variations and ranking examples. Our models and research findings provide new insights on how to utilize external knowledge with deep neural models for response selection and have implications for the design of the next generation of information-seeking conversation systems.