Recent advances in open-domain QA have led to strong models based on dense retrieval, but only focused on retrieving textual passages. In this work, we tackle open-domain QA over tables for the first time, and show that retrieval can be improved by a retriever designed to handle tabular context. We present an effective pre-training procedure for our retriever and improve retrieval quality with mined hard negatives. As relevant datasets are missing, we extract a subset of Natural Questions (Kwiatkowski et al., 2019) into a Table QA dataset. We find that our retriever improves retrieval results from 72.0 to 81.1 recall@10 and end-to-end QA results from 33.8 to 37.7 exact match, over a BERT based retriever.
@article{arxiv.2103.12011,
title = {Open Domain Question Answering over Tables via Dense Retrieval},
author = {Jonathan Herzig and Thomas Müller and Syrine Krichene and Julian Martin Eisenschlos},
journal= {arXiv preprint arXiv:2103.12011},
year = {2021}
}