# A Repository of Conversational Datasets

arXiv: https://arxiv.org/abs/1904.06472 · HTML: https://mlsanity.com/paper/1904.06472/

## Key facts

- Cited by 45 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Apr 2019.

## Built on by (most cited first, 30 of 45)

- [MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge](https://mlsanity.com/paper/2206.08853/) (2022)
- [Survey of Vulnerabilities in Large Language Models Revealed by Adversarial Attacks](https://mlsanity.com/paper/2310.10844/) (2023)
- [RedCaps: web-curated image-text data created by the people, for the people](https://mlsanity.com/paper/2111.11431/) (2021)
- [Dialogue Response Ranking Training with Large-Scale Human Feedback Data](https://mlsanity.com/paper/2009.06978/) (2020)
- [ConvFiT: Conversational Fine-Tuning of Pretrained Language Models](https://mlsanity.com/paper/2109.10126/) (2021)
- [DS-TOD: Efficient Domain Specialization for Task Oriented Dialog](https://mlsanity.com/paper/2110.08395/) (2021)
- [AUGNLG: Few-shot Natural Language Generation using Self-trained Data Augmentation](https://mlsanity.com/paper/2106.05589/) (2021)
- [Identifying Untrustworthy Samples: Data Filtering for Open-domain Dialogues with Bayesian Optimization](https://mlsanity.com/paper/2109.06471/) (2021)
- [A Survey of Intent Classification and Slot-Filling Datasets for Task-Oriented Dialog](https://mlsanity.com/paper/2207.13211/) (2022)
- [CGoDial: A Large-Scale Benchmark for Chinese Goal-oriented Dialog Evaluation](https://mlsanity.com/paper/2211.11617/) (2022)
- [Large Language Models Vote: Prompting for Rare Disease Identification](https://mlsanity.com/paper/2308.12890/) (2023)
- [NLU++: A Multi-Label, Slot-Rich, Generalisable Dataset for Natural Language Understanding in Task-Oriented Dialogue](https://mlsanity.com/paper/2204.13021/) (2022)
- [AARGH! End-to-end Retrieval-Generation for Task-Oriented Dialog](https://mlsanity.com/paper/2209.03632/) (2022)
- [MultiBreak: A Scalable and Diverse Multi-turn Jailbreak Benchmark for Evaluating LLM Safety](https://mlsanity.com/paper/2605.01687/) (2026)
- [Combing for Credentials: Active Pattern Extraction from Smart Reply](https://mlsanity.com/paper/2207.10802/) (2022)
- [Description-Based Text Similarity](https://mlsanity.com/paper/2305.12517/) (2023)
- [Multi-Stage Pre-training Enhanced by ChatGPT for Multi-Scenario Multi-Domain Dialogue Summarization](https://mlsanity.com/paper/2310.10285/) (2023)
- [Learning From Free-Text Human Feedback -- Collect New Datasets Or Extend Existing Ones?](https://mlsanity.com/paper/2310.15758/) (2023)
- [PIPPA: A Partially Synthetic Conversational Dataset](https://mlsanity.com/paper/2308.05884/) (2023)
- [Revealing the Blind Spot of Sentence Encoder Evaluation by HEROS](https://mlsanity.com/paper/2306.05083/) (2023)
- [Representation Learning for Conversational Data using Discourse Mutual Information Maximization](https://mlsanity.com/paper/2112.05787/) (2021)
- [Post-Training Dialogue Summarization using Pseudo-Paraphrasing](https://mlsanity.com/paper/2204.13498/) (2022)
- [A Massive Scale Semantic Similarity Dataset of Historical English](https://mlsanity.com/paper/2306.17810/) (2023)
- [Decoding Human Preferences in Alignment: An Improved Approach to Inverse Constitutional AI](https://mlsanity.com/paper/2501.17112/) (2025)
- [Semantic-based Pre-training for Dialogue Understanding](https://mlsanity.com/paper/2209.09146/) (2022)
- [Linguistic-Based Mild Cognitive Impairment Detection Using Informative Loss](https://mlsanity.com/paper/2402.01690/) (2024)
- [Citegeist: Automated Generation of Related Work Analysis on the arXiv Corpus](https://mlsanity.com/paper/2503.23229/) (2025)
- [On the Robustness of Verbal Confidence of LLMs in Adversarial Attacks](https://mlsanity.com/paper/2507.06489/) (2025)
- [Mixed-effects transformers for hierarchical adaptation](https://mlsanity.com/paper/2205.01749/) (2022)
- [Emotion Eliciting Machine: Emotion Eliciting Conversation Generation based on Dual Generator](https://mlsanity.com/paper/2105.08251/) (2021)

## Abstract (arXiv)

Progress in Machine Learning is often driven by the availability of large datasets, and consistent evaluation metrics for comparing modeling approaches. To this end, we present a repository of conversational datasets consisting of hundreds of millions of examples, and a standardised evaluation procedure for conversational response selection models using '1-of-100 accuracy'. The repository contains scripts that allow researchers to reproduce the standard datasets, or to adapt the pre-processing and data filtering steps to their needs. We introduce and evaluate several competitive baselines for conversational response selection, whose implementations are shared in the repository, as well as a neural encoder model that is trained on the entire training set.