ChunQiuTR: Time-Keyed Temporal Retrieval in Classical Chinese Annals

ACL ARR 2026 January Submission7497 Authors

Keywords: temporal information retrieval, historical NLP, Classical Chinese, reign-based chronology, dense retrieval, dataset

Abstract: Retrieval shapes how language models access and cite knowledge in retrieval-augmented generation (RAG). In historical research, the goal is often to locate the exact record for a specific regnal month, where temporal alignment matters as much as topical relevance. This is especially challenging for Classical Chinese annals: time is encoded in terse, implicit, non-Gregorian reign phrases that are context-dependent, so semantically plausible evidence can still be temporally invalid. We introduce \textbf{ChunQiuTR}, a time-keyed retrieval benchmark built from the \textit{Spring and Autumn Annals} and its exegetical tradition. It organizes records by month-level reign keys and includes chrono-near confounders that mimic real retrieval failures. We propose \textbf{CTD} (Calendrical Temporal Dual-encoder), a time-aware dual-encoder combining Fourier-based absolute context with relative offset biasing. Experiments show consistent gains over semantic dual-encoder baselines under time-keyed evaluation. We will release ChunQiuTR and code after the anonymity period.

Paper Type: Long

Research Area: Resources and Evaluation

Research Area Keywords: Efficient/Low-Resource Methods for NLP, Information Retrieval and Text Mining, Resources and Evaluation, NLP Applications

Contribution Types: NLP engineering experiment, Publicly available software and/or pre-trained models, Data resources, Data analysis

Languages Studied: Classical Chinese, Chinese

Submission Number: 7497