# Interactive Matching Network for Multi-Turn Response Selection in Retrieval-Based Chatbots

arXiv: https://arxiv.org/abs/1901.01824 · HTML: https://mlsanity.com/paper/1901.01824/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jan 2019.

## Built on by (most cited first, 14 of 14)

- [MPC-BERT: A Pre-Trained Language Model for Multi-Party Conversation Understanding](https://mlsanity.com/paper/2106.01541/) (2021)
- [Exploring Dense Retrieval for Dialogue Response Selection](https://mlsanity.com/paper/2110.06612/) (2021)
- [Coarse-to-Fine Knowledge Selection for Document Grounded Dialogs](https://mlsanity.com/paper/2302.11849/) (2023)
- [Small Changes Make Big Differences: Improving Multi-turn Response Selection in Dialogue Systems via Fine-Grained Contrastive Learning](https://mlsanity.com/paper/2111.10154/) (2021)
- [DialGuide: Aligning Dialogue Model Behavior with Developer Guidelines](https://mlsanity.com/paper/2212.10557/) (2022)
- [Multi-turn Dialogue Comprehension from a Topic-aware Perspective](https://mlsanity.com/paper/2309.09666/) (2023)
- [Building an Efficient and Effective Retrieval-based Dialogue System via Mutual Learning](https://mlsanity.com/paper/2110.00159/) (2021)
- [PoE: a Panel of Experts for Generalized Automatic Dialogue Assessment](https://mlsanity.com/paper/2212.08992/) (2022)
- [DialogConv: A Lightweight Fully Convolutional Network for Multi-view Response Selection](https://mlsanity.com/paper/2210.13845/) (2022)
- [Multi-Stage Coarse-to-Fine Contrastive Learning for Conversation Intent Induction](https://mlsanity.com/paper/2303.05034/) (2023)
- [Reranking Overgenerated Responses for End-to-End Task-Oriented Dialogue Systems](https://mlsanity.com/paper/2211.03648/) (2022)
- [Channel-aware Decoupling Network for Multi-turn Dialogue Comprehension](https://mlsanity.com/paper/2301.03953/) (2023)
- [Enhancing Dialogue Systems with Discourse-Level Understanding Using Deep Canonical Correlation Analysis](https://mlsanity.com/paper/2504.09094/) (2025)
- [Learning to Detect Relevant Contexts and Knowledge for Response Selection in Retrieval-based Dialogue Systems](https://mlsanity.com/paper/2509.22845/) (2025)

## Abstract (arXiv)

In this paper, we propose an interactive matching network (IMN) for the multi-turn response selection task. First, IMN constructs word representations from three aspects to address the challenge of out-of-vocabulary (OOV) words. Second, an attentive hierarchical recurrent encoder (AHRE), which is capable of encoding sentences hierarchically and generating more descriptive representations by aggregating with an attention mechanism, is designed. Finally, the bidirectional interactions between whole multi-turn contexts and response candidates are calculated to derive the matching information between them. Experiments on four public datasets show that IMN outperforms the baseline models on all metrics, achieving a new state-of-the-art performance and demonstrating compatibility across domains for multi-turn response selection.