The research addresses the issue of omitted temporal expressions in Japanese news articles, a common problem when processing them as standalone text for search and retrieval-augmented generation (RAG) systems. Without explicit temporal information, large language models can experience temporal mismatches and produce unstable interpretations. This work focuses on generating concrete dates or intervals from these omissions, leveraging the publication date as external context. The jaROTE pipeline, built on existing temporal-expression extraction and normalization techniques, provides a rule-based solution for Japanese news. Results from experiments on two news corpora demonstrate high performance, maintaining competitiveness with LLMs while offering a fast and low-cost pipeline. The research indicates that temporal reproduction improves time-constrained lexical retrieval.
The jaROTE pipeline’s architecture incorporates established techniques for temporal-expression extraction and normalization, refined through manual analysis of Japanese news articles. The system’s key benefit is its reliance on the publication date, providing a consistent and reliable source of temporal context. This approach avoids the need for LLMs to learn complex temporal reasoning patterns, reducing computational demands and improving interpretability.
Experiments on two news corpora showcase the effectiveness of jaROTE. The pipeline achieves high performance, remaining competitive with LLMs. This demonstrates the practical value of publication-date-grounded normalization for Japanese news retrieval applications. The research highlights the potential for improved accuracy and stability in RAG systems when temporal context is explicitly provided.
Source: https://arxiv.org/abs/2609.09569