TempTabQA: Temporal Question Answering for Semi-Structured Tables

Vivek Gupta, Pranshu Kandoi, Mahek Bhavesh Vora, Shuo Zhang, Yujie He, Ridho Reinanda, Vivek Srikumar

Submission Type: Regular Long Paper

Submission Track: Resources and Evaluation

Submission Track 2: Question Answering

Keywords: Semi-structured, Temporal Reasoning, Inference, Question Answering, New Resource, New Dataset, Wikipedia InfoBox

TL;DR: An novel dataset exploring resoning over temporal facts within entity-centric tables

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.

Submission Number: 5195