# Automatic Generation of Conversational Interfaces for Tabular Data Analysis

arXiv: https://arxiv.org/abs/2305.11326 · HTML: https://mlsanity.com/paper/2305.11326/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 17 references that are in the map.
- First posted on arXiv 18 May 2023.

## Builds on (most cited first, 6 of 6)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Tabular Data: Deep Learning is Not All You Need](https://mlsanity.com/paper/2106.03253/) (2021)
- [Bridging Textual and Tabular Data for Cross-Domain Text-to-SQL Semantic Parsing](https://mlsanity.com/paper/2012.12627/) (2020)
- [MultiVision: Designing Analytical Dashboards with Deep Learning Based Recommendation](https://mlsanity.com/paper/2107.07823/) (2021)
- [A Model-based Chatbot Generation Approach to Converse with Open Data Sources](https://mlsanity.com/paper/2007.10503/) (2020)
- [Automatic Generation of Chatbots for Conversational Web Browsing](https://mlsanity.com/paper/2008.12097/) (2020)

## Built on by (most cited first, 2 of 2)

- [A Multimodal Conversational Agent for Tabular Data Analysis](https://mlsanity.com/paper/2511.18405/) (2025)
- [DataWeave: Deploying Human-LLM Analytics for Exploratory Structured Data Analysis](https://mlsanity.com/paper/2610.02679/) (2026)

## Abstract (arXiv)

Tabular data is the most common format to publish and exchange structured data online. A clear example is the growing number of open data portals published by public administrations. However, exploitation of these data sources is currently limited to technical people able to programmatically manipulate and digest such data. As an alternative, we propose the use of chatbots to offer a conversational interface to facilitate the exploration of tabular data sources, including support for data analytics questions that are responded via charts rendered by the chatbot. Moreover, our chatbots are automatically generated from the data source itself thanks to the instantiation of a configurable collection of conversation patterns matched to the chatbot intents and entities.