# Deep Retrieval-Based Dialogue Systems: A Short Review

arXiv: https://arxiv.org/abs/1907.12878 · HTML: https://mlsanity.com/paper/1907.12878/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jul 2019.

## Built on by (most cited first, 11 of 11)

- [DialogVED: A Pre-trained Latent Variable Encoder-Decoder Model for Dialog Response Generation](https://mlsanity.com/paper/2204.13031/) (2022)
- [Dialogue History Matters! Personalized Response Selectionin Multi-turn Retrieval-based Chatbots](https://mlsanity.com/paper/2103.09534/) (2021)
- [ParaQA: A Question Answering Dataset with Paraphrase Responses for Single-Turn Conversation](https://mlsanity.com/paper/2103.07771/) (2021)
- [Contextual Fine-to-Coarse Distillation for Coarse-grained Response Selection in Open-Domain Conversations](https://mlsanity.com/paper/2109.13087/) (2021)
- [Mining Clues from Incomplete Utterance: A Query-enhanced Network for Incomplete Utterance Rewriting](https://mlsanity.com/paper/2307.00866/) (2023)
- [Meta-evaluation of Conversational Search Evaluation Metrics](https://mlsanity.com/paper/2104.13453/) (2021)
- [Towards a Universal NLG for Dialogue Systems and Simulators with Future Bridging](https://mlsanity.com/paper/2105.10267/) (2021)
- [SADAS: A Dialogue Assistant System Towards Remediating Norm Violations in Bilingual Socio-Cultural Conversations](https://mlsanity.com/paper/2402.01736/) (2024)
- [Cognitively Inspired Components for Social Conversational Agents](https://mlsanity.com/paper/2311.05450/) (2023)
- [AdaCoach: A Virtual Coach for Training Customer Service Agents](https://mlsanity.com/paper/2204.12935/) (2022)
- [From Knowledge Augmentation to Multi-tasking: Towards Human-like Dialogue Systems](https://mlsanity.com/paper/2212.03279/) (2022)

## Abstract (arXiv)

Building dialogue systems that naturally converse with humans is being an attractive and an active research domain. Multiple systems are being designed everyday and several datasets are being available. For this reason, it is being hard to keep an up-to-date state-of-the-art. In this work, we present the latest and most relevant retrieval-based dialogue systems and the available datasets used to build and evaluate them. We discuss their limitations and provide insights and guidelines for future work.