Reproducing Omitted Temporal Expressions in Japanese News for Retrieval-Augmented Applications
Japanese news articles frequently contain omitted temporal expressions (day-only or month-only mentions) that require the publication date for proper interpretation, causing temporal mismatches in RAG systems The authors propose jaROTE, a rule-based pipeline that reproduces omitted temporal expressions as concrete dates or intervals using the publication date as external context Experiments on two news corpora show jaROTE achieves high performance and remains competitive with LLMs while offering
Analysis
TL;DR
- Japanese news articles frequently contain omitted temporal expressions (day-only or month-only mentions) that require the publication date for proper interpretation, causing temporal mismatches in RAG systems
- The authors propose jaROTE, a rule-based pipeline that reproduces omitted temporal expressions as concrete dates or intervals using the publication date as external context
- Experiments on two news corpora show jaROTE achieves high performance and remains competitive with LLMs while offering a fast, low-cost alternative
- Temporal reproduction was shown to improve time-constrained lexical retrieval, demonstrating practical value for search and RAG applications
- The approach builds on established temporal-expression extraction and normalization techniques informed by manual analysis of Japanese news articles
Why It Matters
This research addresses a critical gap in retrieval-augmented generation systems where temporal ambiguity in news articles can lead to incorrect or unstable model interpretations. For AI practitioners building search or RAG pipelines over news content, pre-processing temporal expressions before indexing can significantly improve retrieval accuracy and reduce hallucination risks caused by date misunderstandings.
Technical Details
- jaROTE Pipeline: A rule-based system specifically designed for Japanese news that extracts omitted temporal expressions and normalizes them using the publication date as grounding context
- Problem Scope: Focuses on day-only and month-only temporal mentions that are common in Japanese journalism but ambiguous when articles are processed as standalone text
- Methodology: Combines established temporal-expression extraction and normalization techniques with insights from manual analysis of Japanese news corpora
- Evaluation: Tested on two news corpora, demonstrating high performance that remains competitive with LLM-based approaches
- Key Advantage: Provides a fast, low-cost alternative to LLM-based temporal normalization while maintaining strong accuracy
Industry Insight
- News-based RAG systems should incorporate temporal normalization as a preprocessing step before indexing to prevent time-sensitive retrieval failures and LLM misinterpretations
- Rule-based approaches like jaROTE offer a compelling cost-performance tradeoff for domain-specific NLP tasks, suggesting that hybrid systems combining rules with LLMs may outperform pure LLM solutions
- The publication-date-grounded normalization strategy is generalizable beyond Japanese to other languages with similar temporal omission patterns in journalistic writing
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