# Robust (Controlled) Table-to-Text Generation with Structure-Aware Equivariance Learning

arXiv: https://arxiv.org/abs/2205.03972 · HTML: https://mlsanity.com/paper/2205.03972/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- Builds on 40 of its 49 references that are in the map.
- First posted on arXiv 8 May 2022.

## Builds on (most cited first, 30 of 40)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [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)
- [BLEURT: Learning Robust Metrics for Text Generation](https://mlsanity.com/paper/2004.04696/) (2020)
- [Group Equivariant Convolutional Networks](https://mlsanity.com/paper/1602.07576/) (2016)
- [TAPAS: Weakly Supervised Table Parsing via Pre-training](https://mlsanity.com/paper/2004.02349/) (2020)
- [TabFact: A Large-scale Dataset for Table-based Fact Verification](https://mlsanity.com/paper/1909.02164/) (2019)
- [TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data](https://mlsanity.com/paper/2005.08314/) (2020)
- [Harmonic Networks: Deep Translation and Rotation Equivariance](https://mlsanity.com/paper/1612.04642/) (2016)
- [ToTTo: A Controlled Table-To-Text Generation Dataset](https://mlsanity.com/paper/2004.14373/) (2020)
- [HybridQA: A Dataset of Multi-Hop Question Answering over Tabular and Textual Data](https://mlsanity.com/paper/2004.07347/) (2020)
- [Learning SO(3) Equivariant Representations with Spherical CNNs](https://mlsanity.com/paper/1711.06721/) (2017)
- [Understanding image representations by measuring their equivariance and equivalence](https://mlsanity.com/paper/1411.5908/) (2014)
- [Challenges in Data-to-Document Generation](https://mlsanity.com/paper/1707.08052/) (2017)
- [Leveraging Pre-trained Checkpoints for Sequence Generation Tasks](https://mlsanity.com/paper/1907.12461/) (2019)
- [DART: Open-Domain Structured Data Record to Text Generation](https://mlsanity.com/paper/2007.02871/) (2020)
- [Equivariance Through Parameter-Sharing](https://mlsanity.com/paper/1702.08389/) (2017)
- [Text Generation from Knowledge Graphs with Graph Transformers](https://mlsanity.com/paper/1904.02342/) (2019)
- [Handling Divergent Reference Texts when Evaluating Table-to-Text Generation](https://mlsanity.com/paper/1906.01081/) (2019)
- [HiTab: A Hierarchical Table Dataset for Question Answering and Natural Language Generation](https://mlsanity.com/paper/2108.06712/) (2021)
- [TABBIE: Pretrained Representations of Tabular Data](https://mlsanity.com/paper/2105.02584/) (2021)
- [Scale-Equivariant Steerable Networks](https://mlsanity.com/paper/1910.11093/) (2019)
- [Text-to-Text Pre-Training for Data-to-Text Tasks](https://mlsanity.com/paper/2005.10433/) (2020)
- [Logical Natural Language Generation from Open-Domain Tables](https://mlsanity.com/paper/2004.10404/) (2020)
- [INFOTABS: Inference on Tables as Semi-structured Data](https://mlsanity.com/paper/2005.06117/) (2020)
- [Table-to-text Generation by Structure-aware Seq2seq Learning](https://mlsanity.com/paper/1711.09724/) (2017)
- [MATE: Multi-view Attention for Table Transformer Efficiency](https://mlsanity.com/paper/2109.04312/) (2021)

## Built on by (most cited first, 30 of 32)

- [TransTab: Learning Transferable Tabular Transformers Across Tables](https://mlsanity.com/paper/2205.09328/) (2022)
- [Transforming Science with Large Language Models: A Survey on AI-assisted Scientific Discovery, Experimentation, Content Generation, and Evaluation](https://mlsanity.com/paper/2502.05151/) (2025)
- [HYTREL: Hypergraph-enhanced Tabular Data Representation Learning](https://mlsanity.com/paper/2307.08623/) (2023)
- [Rethinking Tabular Data Understanding with Large Language Models](https://mlsanity.com/paper/2312.16702/) (2023)
- [ReasTAP: Injecting Table Reasoning Skills During Pre-training via Synthetic Reasoning Examples](https://mlsanity.com/paper/2210.12374/) (2022)
- [Schema-Driven Information Extraction from Heterogeneous Tables](https://mlsanity.com/paper/2305.14336/) (2023)
- [SpaBERT: A Pretrained Language Model from Geographic Data for Geo-Entity Representation](https://mlsanity.com/paper/2210.12213/) (2022)
- [Long Text and Multi-Table Summarization: Dataset and Method](https://mlsanity.com/paper/2302.03815/) (2023)
- [RAG over Tables: Hierarchical Memory Index, Multi-Stage Retrieval, and Benchmarking](https://mlsanity.com/paper/2504.01346/) (2025)
- [STable: Table Generation Framework for Encoder-Decoder Models](https://mlsanity.com/paper/2206.04045/) (2022)
- [Mitigating Backdoor Threats to Large Language Models: Advancement and Challenges](https://mlsanity.com/paper/2409.19993/) (2024)
- [Salience Allocation as Guidance for Abstractive Summarization](https://mlsanity.com/paper/2210.12330/) (2022)
- [Unifying Structured Data as Graph for Data-to-Text Pre-Training](https://mlsanity.com/paper/2401.01183/) (2024)
- [Robust Natural Language Understanding with Residual Attention Debiasing](https://mlsanity.com/paper/2305.17627/) (2023)
- [Testing the Limits of Unified Sequence to Sequence LLM Pretraining on Diverse Table Data Tasks](https://mlsanity.com/paper/2310.00789/) (2023)
- [MetaSumPerceiver: Multimodal Multi-Document Evidence Summarization for Fact-Checking](https://mlsanity.com/paper/2407.13089/) (2024)
- [Uncovering Limitations of Large Language Models in Information Seeking from Tables](https://mlsanity.com/paper/2406.04113/) (2024)
- [Exploring the Robustness of Language Models for Tabular Question Answering via Attention Analysis](https://mlsanity.com/paper/2406.12719/) (2024)
- [MURMUR: Modular Multi-Step Reasoning for Semi-Structured Data-to-Text Generation](https://mlsanity.com/paper/2212.08607/) (2022)
- [Take a Break in the Middle: Investigating Subgoals towards Hierarchical Script Generation](https://mlsanity.com/paper/2305.10907/) (2023)
- [Improving User Controlled Table-To-Text Generation Robustness](https://mlsanity.com/paper/2302.09820/) (2023)
- [PixT3: Pixel-based Table-To-Text Generation](https://mlsanity.com/paper/2311.09808/) (2023)
- [Think While You Write: Hypothesis Verification Promotes Faithful Knowledge-to-Text Generation](https://mlsanity.com/paper/2311.09467/) (2023)
- [FoodPuzzle: Developing Large Language Model Agents as Flavor Scientists](https://mlsanity.com/paper/2409.12832/) (2024)
- [ReTAG: Reasoning Aware Table to Analytic Text Generation](https://mlsanity.com/paper/2305.11826/) (2023)
- [CATS: A Pragmatic Chinese Answer-to-Sequence Dataset with Large Scale and High Quality](https://mlsanity.com/paper/2306.11477/) (2023)
- [Improving Factual Accuracy of Neural Table-to-Text Output by Addressing Input Problems in ToTTo](https://mlsanity.com/paper/2404.04103/) (2024)
- [Few-Shot Data-to-Text Generation via Unified Representation and Multi-Source Learning](https://mlsanity.com/paper/2308.05317/) (2023)
- [Analysis of Multidomain Abstractive Summarization Using Salience Allocation](https://mlsanity.com/paper/2402.11955/) (2024)
- [Facts-and-Feelings: Capturing both Objectivity and Subjectivity in Table-to-Text Generation](https://mlsanity.com/paper/2406.10560/) (2024)

## Abstract (arXiv)

Controlled table-to-text generation seeks to generate natural language descriptions for highlighted subparts of a table. Previous SOTA systems still employ a sequence-to-sequence generation method, which merely captures the table as a linear structure and is brittle when table layouts change. We seek to go beyond this paradigm by (1) effectively expressing the relations of content pieces in the table, and (2) making our model robust to content-invariant structural transformations. Accordingly, we propose an equivariance learning framework, which encodes tables with a structure-aware self-attention mechanism. This prunes the full self-attention structure into an order-invariant graph attention that captures the connected graph structure of cells belonging to the same row or column, and it differentiates between relevant cells and irrelevant cells from the structural perspective. Our framework also modifies the positional encoding mechanism to preserve the relative position of tokens in the same cell but enforce position invariance among different cells. Our technology is free to be plugged into existing table-to-text generation models, and has improved T5-based models to offer better performance on ToTTo and HiTab. Moreover, on a harder version of ToTTo, we preserve promising performance, while previous SOTA systems, even with transformation-based data augmentation, have seen significant performance drops. Our code is available at https://github.com/luka-group/Lattice.