# NatCS: Eliciting Natural Customer Support Dialogues

arXiv: https://arxiv.org/abs/2305.03007 · HTML: https://mlsanity.com/paper/2305.03007/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 36 references that are in the map.
- First posted on arXiv 4 May 2023.

## Builds on (most cited first, 15 of 15)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [MultiWOZ -- A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling](https://mlsanity.com/paper/1810.00278/) (2018)
- [Towards Scalable Multi-domain Conversational Agents: The Schema-Guided Dialogue Dataset](https://mlsanity.com/paper/1909.05855/) (2019)
- [Efficient Intent Detection with Dual Sentence Encoders](https://mlsanity.com/paper/2003.04807/) (2020)
- [An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction](https://mlsanity.com/paper/1909.02027/) (2019)
- [A Network-based End-to-End Trainable Task-oriented Dialogue System](https://mlsanity.com/paper/1604.04562/) (2016)
- [Taskmaster-1: Toward a Realistic and Diverse Dialog Dataset](https://mlsanity.com/paper/1909.05358/) (2019)
- [Key-Value Retrieval Networks for Task-Oriented Dialogue](https://mlsanity.com/paper/1705.05414/) (2017)
- [Frames: A Corpus for Adding Memory to Goal-Oriented Dialogue Systems](https://mlsanity.com/paper/1704.00057/) (2017)
- [Action-Based Conversations Dataset: A Corpus for Building More In-Depth Task-Oriented Dialogue Systems](https://mlsanity.com/paper/2104.00783/) (2021)
- [Intent Mining from past conversations for conversational agent](https://mlsanity.com/paper/2005.11014/) (2020)
- [Dialog Intent Induction with Deep Multi-View Clustering](https://mlsanity.com/paper/1908.11487/) (2019)
- [Unsupervised Slot Schema Induction for Task-oriented Dialog](https://mlsanity.com/paper/2205.04515/) (2022)
- [A Measure for Dialog Complexity and its Application in Streamlining Service Operations](https://mlsanity.com/paper/1708.04134/) (2017)

## Built on by (most cited first, 5 of 5)

- [Evaluating, Synthesizing, and Enhancing for Customer Support Conversation](https://mlsanity.com/paper/2508.04423/) (2025)
- [Building Customer Support AI Agents at 100M-User Scale: An Evaluation-Driven Framework](https://mlsanity.com/paper/2606.08867/) (2026)
- [CXMArena: Unified Dataset to benchmark performance in realistic CXM Scenarios](https://mlsanity.com/paper/2505.09436/) (2025)
- [Safety as Computation: Certified Answer Reuse via Capability Closure in Task-Oriented Dialogue](https://mlsanity.com/paper/2603.21448/) (2026)
- [Learning Selective LLM Autonomy from Copilot Feedback in Enterprise Customer Support Workflows](https://mlsanity.com/paper/2604.23855/) (2026)

## Abstract (arXiv)

Despite growing interest in applications based on natural customer support conversations, there exist remarkably few publicly available datasets that reflect the expected characteristics of conversations in these settings. Existing task-oriented dialogue datasets, which were collected to benchmark dialogue systems mainly in written human-to-bot settings, are not representative of real customer support conversations and do not provide realistic benchmarks for systems that are applied to natural data. To address this gap, we introduce NatCS, a multi-domain collection of spoken customer service conversations. We describe our process for collecting synthetic conversations between customers and agents based on natural language phenomena observed in real conversations. Compared to previous dialogue datasets, the conversations collected with our approach are more representative of real human-to-human conversations along multiple metrics. Finally, we demonstrate potential uses of NatCS, including dialogue act classification and intent induction from conversations as potential applications, showing that dialogue act annotations in NatCS provide more effective training data for modeling real conversations compared to existing synthetic written datasets. We publicly release NatCS to facilitate research in natural dialog systems