Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023.
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.
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Bib Entry
@inproceedings{gupta2023temptabqa,
author = {Gupta, Vivek and Kandoi, Pranshu and Vora, Mahek and Zhang, Shuo and He, Yujie and Reinanda, Ridho and Srikumar, Vivek},
title = {{T}emp{T}ab{QA}: Temporal Question Answering for Semi-Structured Tables},
booktitle = {Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing},
year = {2023}
}