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To address temporal ambiguities in Japanese retrieval-augmented generation (RAG), the authors build jaROTE, a rule-based pipeline that resolves underspecified temporal expressions鈥攕uch as day- or month-only references鈥攊nto concrete dates and intervals using article publication metadata prior to indexing. Left ungrounded, these omitted temporal cues cause chronic timestamp mismatches and unstable chronological reasoning when retrieved as standalone chunks by downstream LLMs. Evaluated on two Japanese news benchmarks, jaROTE matches LLM-level disambiguation accuracy at significantly lower latency and compute cost while directly boosting time-constrained lexical retrieval performance.
Omitted dates in news articles quietly undermine temporal RAG retrieval, yet a deterministic, rule-based normalizer matches LLM-grade disambiguation at zero inference overhead.
News articles often contain omitted temporal expressions, such as day-only or month-only mentions, which must be interpreted with reference to the publication date. When such articles are indexed or processed as standalone text in search and retrieval-augmented generation (RAG) systems, these omissions can cause temporal mismatches and unstable interpretation by large language models. We focus on reproducing omitted temporal expressions as concrete dates or intervals using the publication date as external context before the articles are indexed for search and RAG applications. Specifically, building on established temporal-expression extraction and normalization techniques and informed by a manual analysis of Japanese news articles, we propose jaROTE, a rule-based pipeline for Japanese news. Experiments on two news corpora demonstrate that jaROTE achieves high performance, and remains competitive with LLMs while providing a fast, low-cost pipeline. We further show that temporal reproduction improves time-constrained lexical retrieval, demonstrating the practical value of publication-date-grounded normalization for Japanese news retrieval.
