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Datasets:
kasys
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ReCaRe

Tasks:
Text Retrieval
Modalities:
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Formats:
json
Languages:
English
Japanese
Size:
100K - 1M
Tags:
legal
law
multilingual
bilingual
retrieval
benchmark
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metadata 
language:
  - en
  - ja
license:
  - cc-by-4.0
  - cc0-1.0
tags:
  - legal
  - law
  - multilingual
  - bilingual
  - retrieval
  - benchmark
  - eu-law
  - japanese-law
language_creators:
  - expert-generated
pretty_name: 'ReCaRe: A Bilingual Legal Benchmark for Revision Candidate Retrieval'
size_categories:
  - 100K<n<1M
source_datasets:
  - original
task_categories:
  - text-retrieval
configs:
  - config_name: corpus-en
    data_files:
      - split: corpus
        path: corpus-en/corpus.jsonl
  - config_name: corpus-ja
    data_files:
      - split: corpus
        path: corpus-ja/corpus.jsonl
  - config_name: queries-rat2rev-en
    data_files:
      - split: queries
        path: queries-rat2rev-en/queries.jsonl
  - config_name: queries-rat2rev-ja
    data_files:
      - split: queries
        path: queries-rat2rev-ja/queries.jsonl
  - config_name: queries-rev2rev-en
    data_files:
      - split: queries
        path: queries-rev2rev-en/queries.jsonl
  - config_name: queries-rev2rev-ja
    data_files:
      - split: queries
        path: queries-rev2rev-ja/queries.jsonl
  - config_name: qrels-rat2rev-en
    data_files:
      - split: train
        path: qrels-rat2rev-en/train.jsonl
      - split: validation
        path: qrels-rat2rev-en/validation.jsonl
      - split: test
        path: qrels-rat2rev-en/test.jsonl
  - config_name: qrels-rat2rev-ja
    data_files:
      - split: train
        path: qrels-rat2rev-ja/train.jsonl
      - split: validation
        path: qrels-rat2rev-ja/validation.jsonl
      - split: test
        path: qrels-rat2rev-ja/test.jsonl
  - config_name: qrels-rev2rev-en
    data_files:
      - split: train
        path: qrels-rev2rev-en/train.jsonl
      - split: validation
        path: qrels-rev2rev-en/validation.jsonl
      - split: test
        path: qrels-rev2rev-en/test.jsonl
  - config_name: qrels-rev2rev-ja
    data_files:
      - split: train
        path: qrels-rev2rev-ja/train.jsonl
      - split: validation
        path: qrels-rev2rev-ja/validation.jsonl
      - split: test
        path: qrels-rev2rev-ja/test.jsonl
  - config_name: metadata-en
    data_files:
      - split: metadata
        path: metadata-en/dataset.jsonl
  - config_name: metadata-ja
    data_files:
      - split: metadata
        path: metadata-ja/dataset.jsonl

ReCaRe: A Bilingual Legal Benchmark for Revision Candidate Retrieval

ReCaRe (pronounced "re-care") is a bilingual legal benchmark for Revision Candidate Retrieval (RCR) — locating the provisions of a legal corpus that constitute plausible candidates for an authoritative revision. It spans European Union law (EUR-Lex, English) and Japanese law (e-Gov, Japanese), with 703 amendment events and ~181k articles, supporting two retrieval tasks (Rat2Rev and Rev2Rev) over bilingual corpora.

Dataset Details

Dataset Description

Document corpora in regulated domains evolve: statutes are amended, internal policies revised, software specifications updated. Yet most information retrieval research has framed retrieval as a one-shot question-answering problem over a frozen corpus, leaving the IR aspects of document maintenance — finding which documents need to change, and which other documents must change with them — comparatively underexplored.

ReCaRe formalizes two complementary RCR tasks over bilingual corpora of EU and Japanese law:

  • Rat2Rev (Rationale-to-Revision Retrieval). Given the textual rationale of a proposed amendment (long, abstract), retrieve the concrete articles that must be modified to implement the amendment.

  • Rev2Rev (Revision-to-Revision Retrieval). Given an article to be revised, retrieve the other articles revised in the same legislative event (co-revised articles).

  • Curated by: Takumi Ito, Yuma Kurokawa (University of Tsukuba), Makoto P. Kato (University of Tsukuba / National Institute of Informatics), Sumio Fujita (LY Corporation).

  • Language(s): English (en, EU subset) and Japanese (ja, Japanese subset).

  • License: CC BY 4.0 (EU subset); CC BY 4.0 / CC0 1.0 (Japanese subset, depending on upstream source).

Uses

Direct Use

ReCaRe is intended as a research benchmark for:

  • Training and evaluating retrieval models on legal text where queries and target documents differ markedly in length and register.
  • Studying document maintenance retrieval: surfacing revision candidates for expert review, distinct from question-answering or paragraph-level retrieval.
  • Comparative benchmarking against general-domain IR resources (e.g. BEIR) to characterise the difficulty of low-overlap, multi-target retrieval with implicit dependency.
  • Ablation of long-context vs. short-context retrieval models, given that Rat2Rev queries are substantially longer than typical IR queries.

Out-of-Scope Use

ReCaRe is not designed for, and should not be used as, a basis for:

  • Automated legal drafting, automated revision recommendation, or any other production legal workflow without expert review. Retrieved articles are candidates for expert review, not authoritative outputs.
  • Provision of legal advice to end-users.
  • Inference of personal or demographic information about individuals — the data contains only public legal text, but personal names appearing in legal records (e.g. drafters, ministers) should not be used to build profiles of those individuals.
  • Generalisation claims about legal systems other than the EU and Japan, or about time periods outside the included windows (EU 2010-2025; JP 2019-2025).

Dataset Structure

The dataset is organised as 12 configs sharing two language-aligned corpora:

Config Splits Schema Records
corpus-en corpus {_id, text} 91,361
corpus-ja corpus {_id, text} 90,170
queries-rat2rev-en queries {_id, text} 340
queries-rat2rev-ja queries {_id, text} 363
queries-rev2rev-en queries {_id, text} 1,509
queries-rev2rev-ja queries {_id, text} 1,653
qrels-rat2rev-en train / validation / test {query-id, corpus-id, score} 2,063 / 1,948 / 2,080
qrels-rat2rev-ja same same 3,228 / 2,501 / 3,395
qrels-rev2rev-en same same 12,088 / 8,189 / 8,156
qrels-rev2rev-ja same same 15,054 / 13,591 / 14,853
metadata-en metadata 16-field amendment metadata (see below) 91,361
metadata-ja metadata same 90,170

All files are JSONL with one JSON object per line. The schemas follow BEIR conventions (_id, text, query-id, corpus-id, score) so that existing tooling such as ir_measures, pytrec_eval, and Pyserini works without adapter code.

metadata-{en,ja} schema (verbatim from the construction pipeline, 16 fields): amendment_law_id, law_id, type_of_change, egov_compare_url, law_title_before, revision_id_before, article_id_before, article_number_before, caption_before, text_before, law_title_after, revision_id_after, article_id_after, article_number_after, caption_after, text_after. Records with amendment_law_id == "None" are unchanged articles (≈85-93%); records with non-None amendment IDs are revisions traceable to a specific amending act. The metadata configs are provided for downstream provenance analyses and are not required to run the retrieval tasks themselves.

Splits. The qrels-* configs use train / validation / test splits. The corpus-*, queries-*, and metadata-* configs are not split — the full corpus is used at retrieval time, and queries split membership is encoded through the qrels.

Quick start.

from datasets import load_dataset

corpus  = load_dataset("kasys/ReCaRe", "corpus-en", split="corpus")
queries = load_dataset("kasys/ReCaRe", "queries-rat2rev-en", split="queries")
qrels   = load_dataset("kasys/ReCaRe", "qrels-rat2rev-en")  # train/validation/test

Dataset Creation

Curation Rationale

Existing legal-IR benchmarks (e.g. COLIEE, BSARD, LeCaRD, STARD, LegalBench-RAG) cover statutory or case-law question answering but not document maintenance — finding which provisions must change when a corpus evolves. ReCaRe was constructed to give the IR community a shared, bilingual benchmark for two practically motivated retrieval tasks (Rat2Rev, Rev2Rev) over legal corpora, in two jurisdictions whose legal-revision practices differ (EU consolidation tradition vs. Japanese amending-act tradition).

Source Data

The dataset contains only public legal text drawn from official portals:

  • EU subset: EUR-Lex (CC BY 4.0 / CC0 re-use) - CELEX-numbered consolidated Regulations, Directives, and Decisions (2010-2025).
  • Japanese subset: e-Gov 法令検索, 日本法令索引, 衆議院議案 (CC BY 4.0 / CC0) - consolidated statutes and amending acts (2019-2025).

Data Collection and Processing

Acquisition spanned 2025-Q3 to 2026-Q1. Full consolidated text was downloaded per act, articles were extracted by official numbering, and explicit amendment events (acts that amend prior acts) were collected. Articles were then aligned with their before/after revisions.

Queries are derived deterministically from this alignment:

  • Rat2Rev queries are the official rationale text of each amending act (one query per amendment).
  • Rev2Rev queries are individual revised articles (up to five queries per amendment, sampled when more revisions exist).

Qrels are likewise derived deterministically: a (rationale, article) pair is positive iff the article is among those revised by that amendment; a (revised-article, article) pair is positive iff both are revised by the same amendment event.

Who are the source data producers?

The original legal text was authored by the European Union legislative bodies (Council, Parliament, Commission, etc.) and by the Government of Japan (Diet, ministries, etc.), and is published in their respective official portals as public law. Producers are state-level institutions acting in a public legislative or regulatory capacity; no individual authorship metadata is included in the dataset beyond what appears in the text of the law itself.

Annotations [optional]

Annotation process

Relevance labels (qrels) are produced automatically from the official amendment alignment described above; there is no per-pair human labelling in the released qrels.

Personal and Sensitive Information

The dataset contains only public legal text released by the European Union and the Japanese government. References to officials, ministers, or other named parties appear because they are part of the public legislative record; they are not subject to research-purpose privacy obligations and have not been anonymised.

Bias, Risks, and Limitations

  • Legal scope. The dataset is grounded in EU and Japanese law and inherits the conventions, drafting traditions, and any latent biases of those legal systems. Conclusions should not be transferred to other jurisdictions or to non-statutory legal text without further validation.
  • Not a substitute for legal advice. The dataset is a research resource. Retrieved articles are revision candidates for expert review.

Glossary [optional]

  • RCR (Revision Candidate Retrieval). The retrieval task of locating documents that constitute plausible candidates for an authoritative revision.
  • Rat2Rev (Rationale-to-Revision Retrieval). Given an amendment's textual rationale, retrieve the concrete articles to be modified.
  • Rev2Rev (Revision-to-Revision Retrieval). Given an article to be revised, retrieve the other articles revised in the same amendment event.
  • Amendment event. One amending act (a single Regulation, Directive, or domestic amending law) that modifies one or more prior provisions.