TempTabQA: Temporal Question Answering for Semi-Structured Tables
Vivek Gupta, Pranshu Kandoi, Mahek Bhavesh Vora, Shuo Zhang, Yujie He, Ridho Reinanda, Vivek Srikumar
Abstract
Semi-structured data, such as Infobox tables, often include temporal information about entities, either implicitly or explicitly. Can current NLP systems reason about such information in semi-structured tables? To tackle this question, we introduce the task of temporal question answering on semi-structured tables. We present a dataset, TEMPTABQA, which comprises 11,454 question-answer pairs extracted from 1,208 Wikipedia Infobox tables spanning more than 90 distinct domains. Using this dataset, we evaluate several state-of-the-art models for temporal reasoning. We observe that even the top-performing LLMs lag behind human performance by more than 13.5 F1 points. Given these results, our dataset has the potential to serve as a challenging benchmark to improve the temporal reasoning capabilities of NLP models.
BibTeX
@inproceedings{
gupta2023temptabqa,
title={TempTab{QA}: Temporal Question Answering for Semi-Structured Tables},
author={Vivek Gupta and Pranshu Kandoi and Mahek Bhavesh Vora and Shuo Zhang and Yujie He and Ridho Reinanda and Vivek Srikumar},
booktitle={The 2023 Conference on Empirical Methods in Natural Language Processing},
year={2023},
url={https://openreview.net/forum?id=q8aTDcIXnO}
}