# Improving User Controlled Table-To-Text Generation Robustness

arXiv: https://arxiv.org/abs/2302.09820 · HTML: https://mlsanity.com/paper/2302.09820/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 24 references that are in the map.
- First posted on arXiv 20 Feb 2023.

## Builds on (most cited first, 12 of 12)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Get To The Point: Summarization with Pointer-Generator Networks](https://mlsanity.com/paper/1704.04368/) (2017)
- [ToTTo: A Controlled Table-To-Text Generation Dataset](https://mlsanity.com/paper/2004.14373/) (2020)
- [Data-to-Text Generation with Content Selection and Planning](https://mlsanity.com/paper/1809.00582/) (2018)
- [Text-to-Text Pre-Training for Data-to-Text Tasks](https://mlsanity.com/paper/2005.10433/) (2020)
- [Learning Neural Templates for Text Generation](https://mlsanity.com/paper/1808.10122/) (2018)
- [Plan-then-Generate: Controlled Data-to-Text Generation via Planning](https://mlsanity.com/paper/2108.13740/) (2021)
- [Robust (Controlled) Table-to-Text Generation with Structure-Aware Equivariance Learning](https://mlsanity.com/paper/2205.03972/) (2022)
- [Automatic Construction of Evaluation Suites for Natural Language Generation Datasets](https://mlsanity.com/paper/2106.09069/) (2021)
- [Bootstrapping Generators from Noisy Data](https://mlsanity.com/paper/1804.06385/) (2018)

## Built on by (most cited first, 6 of 6)

- [Schema-Driven Information Extraction from Heterogeneous Tables](https://mlsanity.com/paper/2305.14336/) (2023)
- [GLProtein: Global-and-Local Structure Aware Protein Representation Learning](https://mlsanity.com/paper/2506.06294/) (2025)
- [Self-Training with Pseudo-Label Scorer for Aspect Sentiment Quad Prediction](https://mlsanity.com/paper/2406.18078/) (2024)
- [Improving Factual Accuracy of Neural Table-to-Text Output by Addressing Input Problems in ToTTo](https://mlsanity.com/paper/2404.04103/) (2024)
- [Enhancing Molecular Property Predictions by Learning from Bond Modelling and Interactions](https://mlsanity.com/paper/2603.00568/) (2026)
- [Geometric Flow Matching for Molecular Conformation Generation via Manifold Decomposition](https://mlsanity.com/paper/2605.25577/) (2026)

## Abstract (arXiv)

In this work we study user controlled table-to-text generation where users explore the content in a table by selecting cells and reading a natural language description thereof automatically produce by a natural language generator. Such generation models usually learn from carefully selected cell combinations (clean cell selections); however, in practice users may select unexpected, redundant, or incoherent cell combinations (noisy cell selections). In experiments, we find that models perform well on test sets coming from the same distribution as the train data but their performance drops when evaluated on realistic noisy user inputs. We propose a fine-tuning regime with additional user-simulated noisy cell selections. Models fine-tuned with the proposed regime gain 4.85 BLEU points on user noisy test cases and 1.4 on clean test cases; and achieve comparable state-of-the-art performance on the ToTTo dataset.