# It was the training data pruning too!

arXiv: https://arxiv.org/abs/1803.04579 · HTML: https://mlsanity.com/paper/1803.04579/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Mar 2018.

## Built on by (most cited first, 1 of 1)

- [Uniform-in-Phase-Space Data Selection with Iterative Normalizing Flows](https://mlsanity.com/paper/2112.15446/) (2021)

## Abstract (arXiv)

We study the current best model (KDG) for question answering on tabular data evaluated over the WikiTableQuestions dataset. Previous ablation studies performed against this model attributed the model's performance to certain aspects of its architecture. In this paper, we find that the model's performance also crucially depends on a certain pruning of the data used to train the model. Disabling the pruning step drops the accuracy of the model from 43.3% to 36.3%. The large impact on the performance of the KDG model suggests that the pruning may be a useful pre-processing step in training other semantic parsers as well.