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,

翻译：时间在信息的生成、检索与解释中扮演着关键角色。本综述全面梳理了时序问答（Temporal Question Answering, TQA）这一研究领域，该领域专注于回答涉及时序约束或上下文的问题。随着来自新闻文章、网络存档和知识库等来源的带时间戳内容持续增长，TQA系统需应对诸如检测时间意图、标准化时间表达、排序事件以及对演化或模糊事实进行推理等挑战。我们通过一个统一视角组织现有工作，该视角捕捉了语料时间性、问题时间性与模型能力之间的交互，从而实现对数据集、任务和方法的系统性比较。我们回顾了神经架构（尤其是基于Transformer的模型和大语言模型）推动下TQA的最新进展，重点阐述了时序语言建模、检索增强生成（RAG）及时序推理方面的突破。同时，本文还讨论了用于测试时序鲁棒性的基准数据集与评估策略。