TempTabQA: Temporal Question Answering for Semi-Structured Tables

Autor:	Gupta, Vivek, Kandoi, Pranshu, Vora, Mahek Bhavesh, Zhang, Shuo, He, Yujie, Reinanda, Ridho, Srikumar, Vivek
Rok vydání:	2023
Předmět:	Computer Science - Computation and Language Computer Science - Artificial Intelligence Computer Science - Information Retrieval
Druh dokumentu:	Working Paper
Popis:	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. Comment: EMNLP 2023(Main), 23 Figures, 32 Tables
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2311.08002 Zobrazit plný text záznamu View this record from Arxiv