# Table2Charts: Recommending Charts by Learning Shared Table Representations

arXiv: https://arxiv.org/abs/2008.11015 · HTML: https://mlsanity.com/paper/2008.11015/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Aug 2020.

## Built on by (most cited first, 14 of 14)

- [Table Pre-training: A Survey on Model Architectures, Pre-training Objectives, and Downstream Tasks](https://mlsanity.com/paper/2201.09745/) (2022)
- [Text2Analysis: A Benchmark of Table Question Answering with Advanced Data Analysis and Unclear Queries](https://mlsanity.com/paper/2312.13671/) (2023)
- [HAIChart: Human and AI Paired Visualization System](https://mlsanity.com/paper/2406.11033/) (2024)
- [LLM4Vis: Explainable Visualization Recommendation using ChatGPT](https://mlsanity.com/paper/2310.07652/) (2023)
- [Generative AI for Visualization: State of the Art and Future Directions](https://mlsanity.com/paper/2404.18144/) (2024)
- [LightVA: Lightweight Visual Analytics with LLM Agent-Based Task Planning and Execution](https://mlsanity.com/paper/2411.05651/) (2024)
- [AnaMeta: A Table Understanding Dataset of Field Metadata Knowledge Shared by Multi-dimensional Data Analysis Tasks](https://mlsanity.com/paper/2209.00946/) (2022)
- [Agnostic Visual Recommendation Systems: Open Challenges and Future Directions](https://mlsanity.com/paper/2302.00569/) (2023)
- [TableLoRA: Low-rank Adaptation on Table Structure Understanding for Large Language Models](https://mlsanity.com/paper/2503.04396/) (2025)
- [Reinforced Labels: Multi-Agent Deep Reinforcement Learning for Point-Feature Label Placement](https://mlsanity.com/paper/2303.01388/) (2023)
- [Adversarial Attacks on Machine Learning-Aided Visualizations](https://mlsanity.com/paper/2409.02485/) (2024)
- [ASTA: Learning Analytical Semantics over Tables for Intelligent Data Analysis and Visualization](https://mlsanity.com/paper/2208.01043/) (2022)
- [Data Therapist: Eliciting Domain Knowledge from Subject Matter Experts Using Large Language Models](https://mlsanity.com/paper/2505.00455/) (2025)
- [DEEPCHART: How Far are LLMs from Faithful Data-Science Chart Generation?](https://mlsanity.com/paper/2608.26757/) (2026)

## Abstract (arXiv)

It is common for people to create different types of charts to explore a multi-dimensional dataset (table). However, to recommend commonly composed charts in real world, one should take the challenges of efficiency, imbalanced data and table context into consideration. In this paper, we propose Table2Charts framework which learns common patterns from a large corpus of (table, charts) pairs. Based on deep Q-learning with copying mechanism and heuristic searching, Table2Charts does table-to-sequence generation, where each sequence follows a chart template. On a large spreadsheet corpus with 165k tables and 266k charts, we show that Table2Charts could learn a shared representation of table fields so that recommendation tasks on different chart types could mutually enhance each other. Table2Charts outperforms other chart recommendation systems in both multi-type task (with doubled recall numbers R@3=0.61 and R@1=0.43) and human evaluations.