系统梳理时间问答技术，应对时间信息理解与推理挑战
It's High Time: A Survey of Temporal Question Answering
- 构建统一框架分析时间语料、问题与模型的交互关系
- 突破时间表达归一化与事件排序难题，提升时序推理能力
- 适合关注时序理解、知识增强生成的AI研究者阅读
时间在信息生成、检索与解读中起着关键作用。本文全面综述时间问答（Temporal Question Answering, TQA）领域，聚焦于包含时间约束或上下文的问题回答。随着新闻文章、网络存档和知识库等带时间戳内容持续增长，TQA系统需应对时间意图识别、时间表达归一化、事件排序以及对动态或模糊事实的推理等挑战。本文通过统一视角，揭示语料时间性、问题时间性与模型能力之间的互动关系，实现对数据集、任务与方法的系统性对比。综述了基于神经架构（尤其是基于Transformer的模型和大语言模型）在TQA中的最新进展，重点包括时间语言建模、检索增强生成（RAG）与时间推理。同时讨论了用于测试时间鲁棒性的基准数据集与评估策略。
原文摘要 · Abstract (English)
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,
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