Paper
TempTabQA: Temporal Question Answering for Semi-Structured Tables
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 Wikip · Authors: Vivek Gupta, Pranshu Kandoi, Mahek Bhavesh Vora, Shuo Zhang, Yujie He, Ridho Reinanda… · Published 2023-11-14 · arXiv:2311.08002
- Published
- 2023-11-14
- Authors
- 7
- arXiv
- 2311.08002
- Category
- cs.CL
Abstract
arXiv:2311.08002
Vivek Gupta, Pranshu Kandoi, Mahek Bhavesh Vora, Shuo Zhang, Yujie He, Ridho Reinanda, Vivek Srikumar
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.