# Adversarial TableQA: Attention Supervision for Question Answering on Tables

arXiv: https://arxiv.org/abs/1810.08113 · HTML: https://mlsanity.com/paper/1810.08113/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Oct 2018.

## Built on by (most cited first, 10 of 10)

- [ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning](https://mlsanity.com/paper/2203.10244/) (2022)
- [Going Full-TILT Boogie on Document Understanding with Text-Image-Layout Transformer](https://mlsanity.com/paper/2102.09550/) (2021)
- [QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehension](https://mlsanity.com/paper/2107.12708/) (2021)
- [Explanation-Based Human Debugging of NLP Models: A Survey](https://mlsanity.com/paper/2104.15135/) (2021)
- [RobuT: A Systematic Study of Table QA Robustness Against Human-Annotated Adversarial Perturbations](https://mlsanity.com/paper/2306.14321/) (2023)
- [A Survey on Table Question Answering: Recent Advances](https://mlsanity.com/paper/2207.05270/) (2022)
- [Business Document Information Extraction: Towards Practical Benchmarks](https://mlsanity.com/paper/2206.11229/) (2022)
- [cTBLS: Augmenting Large Language Models with Conversational Tables](https://mlsanity.com/paper/2303.12024/) (2023)
- [TableQuery: Querying tabular data with natural language](https://mlsanity.com/paper/2202.00454/) (2022)
- [BreakingBERT@IITK at SemEval-2021 Task 9 : Statement Verification and Evidence Finding with Tables](https://mlsanity.com/paper/2104.03071/) (2021)

## Abstract (arXiv)

The task of answering a question given a text passage has shown great developments on model performance thanks to community efforts in building useful datasets. Recently, there have been doubts whether such rapid progress has been based on truly understanding language. The same question has not been asked in the table question answering (TableQA) task, where we are tasked to answer a query given a table. We show that existing efforts, of using "answers" for both evaluation and supervision for TableQA, show deteriorating performances in adversarial settings of perturbations that do not affect the answer. This insight naturally motivates to develop new models that understand question and table more precisely. For this goal, we propose Neural Operator (NeOp), a multi-layer sequential network with attention supervision to answer the query given a table. NeOp uses multiple Selective Recurrent Units (SelRUs) to further help the interpretability of the answers of the model. Experiments show that the use of operand information to train the model significantly improves the performance and interpretability of TableQA models. NeOp outperforms all the previous models by a big margin.