It's High Time: A Survey of Temporal Question Answering

TL;DR

This survey comprehensively reviews Temporal Question Answering (TQA), focusing on challenges, recent neural approaches, and benchmarks for answering questions involving temporal information.

Contribution

It provides a unified perspective on TQA, comparing datasets, tasks, and approaches, and highlights recent advances with neural models and LLMs.

Findings

Progress in temporal language modeling and reasoning.

Development of benchmark datasets and evaluation strategies.

Advances enabled by transformer-based models and LLMs.

Abstract

Time plays a critical role in how information is generated, retrieved, and interpreted. In this survey, we provide a comprehensive overview of Temporal Question Answering (TQA), a research area that focuses on answering questions involving temporal constraints or context. As time-stamped content from sources like news articles, web archives, and knowledge bases continues to grow, TQA systems must address challenges such as detecting temporal intent, normalizing time expressions, ordering events, and reasoning over evolving or ambiguous facts. We organize existing work through a unified perspective that captures the interaction between corpus temporality, question temporality, and model capabilities, enabling a systematic comparison of datasets, tasks, and approaches. We review recent advances in TQA enabled by neural architectures, especially transformer-based models and Large Language Models (LLMs), highlighting progress in temporal language modeling, retrieval-augmented generation (RAG), and temporal reasoning. We also discuss benchmark datasets and evaluation strategies designed to test temporal robustness,