Reproducing Omitted Temporal Expressions in Japanese News for Retrieval-Augmented Applications
Tomoaki Yasuda, Shotaro Ishihara
cs.CL
Quick verdict
jaROTE rule-based pipeline reproduces omitted Japanese news dates (F1 0.96/0.89 Nikkei/Livedoor); solid honest work, though eval data overlaps rule design.
Summary
News articles frequently omit temporal components ('the 21st', 'March'), leaving text ambiguous once indexed standalone in search or RAG systems. The authors manually analyze 97 Nikkei Newspaper Article Open Corpus articles, find 303 omitted temporal expressions, and show 256 (84.5%) fall into five categories reproducible from the publication date. They propose jaROTE, a deterministic rule-based pipeline extending the ja-timex parser with masking, range-endpoint completion, multi-reference-date parsing, and context-based date selection. Extraction F1 rises from 0.6938 to 0.9903 on Nikkei and from 0.5094 to 0.9572 on Livedoor versus raw ja-timex; fixed-span normalization accuracy is 0.9710/0.9431 versus GPT-4o-mini's 1.0000/0.9704, while processing takes milliseconds rather than seconds per article. End-to-end F1 is 0.9631 (Nikkei) and 0.8905 (Livedoor). On 133 time-constrained queries, reproduction consistently improves TF-IDF/BM25 recall@k; dense-retriever gains vary by embedding model. Key limitations, acknowledged by the authors: rules were designed on the same or adjacent evaluation corpora, so generalization is unverified; discourse-level reference-time tracking is absent; no statistical testing is reported; and code and gold annotations remain unreleased.
Review complete
Assessment at a glance
5
risk flags identified
● high 0
● medium 3
● low 2
5 strengths · 5 weaknesses · 5 author questions
AI text signal = Binoculars zero-shot detector score over 20 sampled passages (Fast-DetectGPT co-reported) — absence does not prove human authorship, and a flag does not imply misconduct.
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Key claims under review
- Five rule-addressable categories (Incomplete, Relative, Range, Composite, Japanese era) cover 256 of 303 (84.5%) omitted temporal expressions found in 97 Nikkei articles.
- jaROTE achieves end-to-end temporal reproduction F1 of 0.9631 on Nikkei and 0.8905 on Livedoor without any LLM inference, at 0.01–0.02 seconds per article.
- In fixed-span normalization, jaROTE is within a few points of GPT-4o/GPT-4o-mini accuracy while being orders of magnitude faster and cheaper.
- Publication-date-grounded reproduction improves time-constrained lexical retrieval (TF-IDF/BM25) recall@k more consistently than simply appending the publication date to the document.
- A small minority of target expressions (3/256, 1.2%) require discourse-level reference-time updates that jaROTE does not handle.
Strengths
Well-scoped task definition grounded in data
Manual analysis of 97 Nikkei articles (Table 1) yields a defensible coverage claim: five categories capture 256 of 303 (84.5%) omitted expressions, with explicit exclusion criteria for deictic, seasonal, and comparison types.
Large, cleanly isolated extraction gains
Against raw ja-timex, jaROTE raises extraction F1 from 0.6938 to 0.9903 on Nikkei (FP 226→4) and 0.5094 to 0.9572 on Livedoor (FP 886→37) with recall held near 1.0 (Table 2), isolating the value of news-specific masking and endpoint completion.
Quantified cost/latency case against LLMs
Unusually candid limitations
Reproducibility-friendly appendices
Weaknesses
Evaluation circularity undermines generalization claims
No statistical evidence for comparative claims
LLM accuracy superiority understated in framing
Closed code and gold data block exact replication
Lenient span matching and single-annotator gold
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Scope & Claims
The paper tackles a concrete deployment problem: Japanese news routinely omits calendar components ('21日', '3月') that readers resolve via the publication date, but standalone-indexed chunks in search/RAG systems cannot (Figure 1's prime-minister example). From manual analysis of 97 articles of the Nikkei Newspaper Article Open Corpus — 303 omitted temporal expressions, of which 256 (84.5%) fall into five categories reproducible from the publication date (Table 1) — the authors claim three contributions: (1) the category analysis with explicit inclusion/exclusion criteria (deictic, seasonal, and comparison expressions are excluded by design); (2) jaROTE, a deterministic rule pipeline 'competitive with LLMs' at far lower cost; (3) evidence that reproduction improves time-constrained retrieval. Claims are mostly calibrated to the evidence. One framing tension: the abstract's 'competitive with LLMs' is a cost argument — in pure accuracy terms GPT-4o-mini wins both normalization settings (Table 3).
The remaining sections cover Methodology Assessment, Statistical Evidence, Results & Reproducibility, Limitations & Risks, and Verdict & Recommendations.
- All 5 strengths & 5 weaknesses, analyzed in detail
- 5 risk flags with severity, section & reviewer questions
- Methods & statistical evidence assessment
- 5 questions a peer reviewer would ask
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Frequently asked questions
What is jaROTE and what does it do?
jaROTE is a rule-based, LLM-free preprocessing pipeline for Japanese news that rewrites omitted temporal expressions (e.g., 'the 21st', 'March') into full dates or intervals, using the article's publication date as the reference. It extends the open-source ja-timex parser with news-specific masking, range-endpoint completion, multi-reference-date parsing, and context-based date selection, processing each article in roughly 0.01–0.02 seconds.
Why do omitted dates cause problems for RAG and search systems?
When a news article is split into standalone chunks for indexing, the publication-date context needed to interpret expressions like 'the 21st' is lost. Time-constrained queries (e.g., 'Who was designated prime minister on October 21, 2025?') then fail to match the chunk text. The paper shows that rewriting such expressions into explicit dates before indexing improves TF-IDF and BM25 recall on 133 time-constrained queries.
How does jaROTE compare with using GPT-4o for date normalization?
On fixed-span normalization, GPT-4o-mini is more accurate (1.0000 on Nikkei, 0.9704 on Livedoor) than jaROTE (0.9710/0.9431), but jaROTE is deterministic, needs no inference cost, and processes an article in milliseconds versus 1.76–5.71 seconds for the LLMs. End-to-end, jaROTE reaches F1 0.9631 on Nikkei and 0.8905 on Livedoor.
Is the jaROTE code or dataset publicly available?
Not at the time of submission: the authors state that processing code and some auxiliary datasets are unreleased due to legal and operational constraints. The underlying ja-timex parser is open source, the Livedoor corpus is publicly downloadable, and the Nikkei corpus is available under license; the paper's appendices document rules, prompts, and training setup in detail.
About this review
This is an AI-generated preliminary review by Evolve.Pub, produced from the openly available text of the paper. It is not peer review and does not represent the authors' views. The canonical record of the paper is at the publisher link above.
- The provided text is a sampled excerpt with [...sampling gap...] markers: the numeric contents of the retrieval results table (Table 6) and parts of Section 2, Section 4.3, and Section 6 are not visible; retrieval judgments rely on the Section 6.2 prose.
- Annotator count and inter-annotator agreement for gold spans/normalized values are not visible in the provided excerpt; the single-annotator concern assumes none was reported inside the sampling gaps.
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