# NaturalConv: A Chinese Dialogue Dataset Towards Multi-turn Topic-driven Conversation

arXiv: https://arxiv.org/abs/2103.02548 · HTML: https://mlsanity.com/paper/2103.02548/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 31 references that are in the map.
- First posted on arXiv 3 Mar 2021.

## Builds on (most cited first, 19 of 19)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [A Diversity-Promoting Objective Function for Neural Conversation Models](https://mlsanity.com/paper/1510.03055/) (2015)
- [Recipes for building an open-domain chatbot](https://mlsanity.com/paper/2004.13637/) (2020)
- [Wizard of Wikipedia: Knowledge-Powered Conversational agents](https://mlsanity.com/paper/1811.01241/) (2018)
- [Towards a Human-like Open-Domain Chatbot](https://mlsanity.com/paper/2001.09977/) (2020)
- [Emotional Chatting Machine: Emotional Conversation Generation with Internal and External Memory](https://mlsanity.com/paper/1704.01074/) (2017)
- [Neural Approaches to Conversational AI](https://mlsanity.com/paper/1809.08267/) (2018)
- [Challenges in Building Intelligent Open-domain Dialog Systems](https://mlsanity.com/paper/1905.05709/) (2019)
- [A Knowledge-Grounded Neural Conversation Model](https://mlsanity.com/paper/1702.01932/) (2017)
- [A Dataset for Document Grounded Conversations](https://mlsanity.com/paper/1809.07358/) (2018)
- [Proactive Human-Machine Conversation with Explicit Conversation Goals](https://mlsanity.com/paper/1906.05572/) (2019)
- [KdConv: A Chinese Multi-domain Dialogue Dataset Towards Multi-turn Knowledge-driven Conversation](https://mlsanity.com/paper/2004.04100/) (2020)
- [Towards Exploiting Background Knowledge for Building Conversation Systems](https://mlsanity.com/paper/1809.08205/) (2018)
- [An Interpretable Reasoning Network for Multi-Relation Question Answering](https://mlsanity.com/paper/1801.04726/) (2018)
- [DyKgChat: Benchmarking Dialogue Generation Grounding on Dynamic Knowledge Graphs](https://mlsanity.com/paper/1910.00610/) (2019)
- [Conversing by Reading: Contentful Neural Conversation with On-demand Machine Reading](https://mlsanity.com/paper/1906.02738/) (2019)
- [Flexible End-to-End Dialogue System for Knowledge Grounded Conversation](https://mlsanity.com/paper/1709.04264/) (2017)
- [An Effective Domain Adaptive Post-Training Method for BERT in Response Selection](https://mlsanity.com/paper/1908.04812/) (2019)

## Built on by (most cited first, 30 of 35)

- [PanGu-Sigma: Towards Trillion Parameter Language Model with Sparse Heterogeneous Computing](https://mlsanity.com/paper/2303.10845/) (2023)
- [EVA2.0: Investigating Open-Domain Chinese Dialogue Systems with Large-Scale Pre-Training](https://mlsanity.com/paper/2203.09313/) (2022)
- [Improving Unsupervised Dialogue Topic Segmentation with Utterance-Pair Coherence Scoring](https://mlsanity.com/paper/2106.06719/) (2021)
- [CHBias: Bias Evaluation and Mitigation of Chinese Conversational Language Models](https://mlsanity.com/paper/2305.11262/) (2023)
- [OpenCharacter: Training Customizable Role-Playing LLMs with Large-Scale Synthetic Personas](https://mlsanity.com/paper/2501.15427/) (2025)
- [Uncovering the Potential of ChatGPT for Discourse Analysis in Dialogue: An Empirical Study](https://mlsanity.com/paper/2305.08391/) (2023)
- [BEYOND DIALOGUE: A Profile-Dialogue Alignment Framework Towards General Role-Playing Language Model](https://mlsanity.com/paper/2408.10903/) (2024)
- [Where to Go for the Holidays: Towards Mixed-Type Dialogs for Clarification of User Goals](https://mlsanity.com/paper/2204.07299/) (2022)
- [Domain-Adaptive Pretraining Methods for Dialogue Understanding](https://mlsanity.com/paper/2105.13665/) (2021)
- [Interpersonal Memory Matters: A New Task for Proactive Dialogue Utilizing Conversational History](https://mlsanity.com/paper/2503.05150/) (2025)
- [BabyBabelLM: A Multilingual Benchmark of Developmentally Plausible Training Data](https://mlsanity.com/paper/2510.10159/) (2025)
- [GLM-Dialog: Noise-tolerant Pre-training for Knowledge-grounded Dialogue Generation](https://mlsanity.com/paper/2302.14401/) (2023)
- [Multi-Granularity Prompts for Topic Shift Detection in Dialogue](https://mlsanity.com/paper/2305.14006/) (2023)
- [H2HMem: A Multimodal Memory Benchmark for Agents in Human-Human Interactions](https://mlsanity.com/paper/2606.09461/) (2026)
- [CSAGN: Conversational Structure Aware Graph Network for Conversational Semantic Role Labeling](https://mlsanity.com/paper/2109.11541/) (2021)
- [We Argue to Agree: Towards Personality-Driven Argumentation-Based Negotiation Dialogue Systems for Tourism](https://mlsanity.com/paper/2509.11118/) (2025)
- [MOA: Multi-Objective Alignment for Role-Playing Agents](https://mlsanity.com/paper/2512.09756/) (2025)
- [Topic Shift Detection in Chinese Dialogues: Corpus and Benchmark](https://mlsanity.com/paper/2305.01195/) (2023)
- [Fostering Natural Conversation in Large Language Models with NICO: a Natural Interactive COnversation dataset](https://mlsanity.com/paper/2408.09330/) (2024)
- [CORECODE: A Common Sense Annotated Dialogue Dataset with Benchmark Tasks for Chinese Large Language Models](https://mlsanity.com/paper/2312.12853/) (2023)
- [Inconsistent dialogue responses and how to recover from them](https://mlsanity.com/paper/2401.10353/) (2024)
- [MQM-Chat: Multidimensional Quality Metrics for Chat Translation](https://mlsanity.com/paper/2408.16390/) (2024)
- [Enabling Harmonious Human-Machine Interaction with Visual-Context Augmented Dialogue System: A Review](https://mlsanity.com/paper/2207.00782/) (2022)
- [Exploring Effective Information Utilization in Multi-Turn Topic-Driven Conversations](https://mlsanity.com/paper/2209.00250/) (2022)
- [Interpreting Indirect Answers to Yes-No Questions in Multiple Languages](https://mlsanity.com/paper/2310.13290/) (2023)
- [INA: An Integrative Approach for Enhancing Negotiation Strategies with Reward-Based Dialogue System](https://mlsanity.com/paper/2310.18207/) (2023)
- [MUSE: Multi-Domain Chinese User Simulation via Self-Evolving Profiles and Rubric-Guided Alignment](https://mlsanity.com/paper/2604.13828/) (2026)
- [A Chit-Chats Enhanced Task-Oriented Dialogue Corpora for Fuse-Motive Conversation Systems](https://mlsanity.com/paper/2205.05886/) (2022)
- [A Knowledge Plug-and-Play Test Bed for Open-domain Dialogue Generation](https://mlsanity.com/paper/2403.03496/) (2024)
- [Krul: Efficient State Restoration for Multi-turn Conversations with Dynamic Cross-layer KV Sharing](https://mlsanity.com/paper/2507.08045/) (2025)

## Abstract (arXiv)

In this paper, we propose a Chinese multi-turn topic-driven conversation dataset, NaturalConv, which allows the participants to chat anything they want as long as any element from the topic is mentioned and the topic shift is smooth. Our corpus contains 19.9K conversations from six domains, and 400K utterances with an average turn number of 20.1. These conversations contain in-depth discussions on related topics or widely natural transition between multiple topics. We believe either way is normal for human conversation. To facilitate the research on this corpus, we provide results of several benchmark models. Comparative results show that for this dataset, our current models are not able to provide significant improvement by introducing background knowledge/topic. Therefore, the proposed dataset should be a good benchmark for further research to evaluate the validity and naturalness of multi-turn conversation systems. Our dataset is available at https://ailab.tencent.com/ailab/nlp/dialogue/#datasets.