TempTabQA: Temporal Question Answering for Semi-Structured Tables
Vivek Gupta, Pranshu Kandoi, Mahek Bhavesh Vora, Shuo Zhang, Yujie He, Ridho Reinanda, Vivek Srikumar · 2023
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-ofthe-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.