| --- |
| |
| |
| 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 |
|
|
| <!-- Provide a quick summary of the dataset. --> |
|
|
| 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 |
|
|
| <!-- Provide a longer summary of what this dataset is. --> |
|
|
| 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 |
|
|
| <!-- Address questions around how the dataset is intended to be used. --> |
|
|
| ### Direct Use |
|
|
| <!-- This section describes suitable use cases for the dataset. --> |
|
|
| 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 |
|
|
| <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. --> |
|
|
| 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 |
|
|
| <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. --> |
|
|
| 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.** |
|
|
| ```python |
| 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 |
|
|
| <!-- Motivation for the creation of this dataset. --> |
|
|
| 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 |
|
|
| <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> |
|
|
| 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 |
|
|
| <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> |
|
|
| 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? |
|
|
| <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> |
|
|
| 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] |
|
|
| <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. --> |
|
|
| #### Annotation process |
|
|
| <!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. --> |
|
|
| 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 |
|
|
| <!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> |
|
|
| 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 |
|
|
| <!-- This section is meant to convey both technical and sociotechnical 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] |
|
|
| <!-- If relevant, include terms and calculations in this section that can help readers understand the dataset or dataset card. --> |
|
|
| - **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. |
