docs: refresh data card (author edits)

README.md

@@ -16,8 +16,6 @@ tags:
   - benchmark
   - eu-law
   - japanese-law
-annotations_creators:
-  - expert-generated
 language_creators:
   - expert-generated
 pretty_name: ReCaRe — A Bilingual Legal Benchmark for Revision Candidate Retrieval
@@ -94,7 +92,7 @@ configs:
         path: metadata-ja/dataset.jsonl
 ---
 
-# Dataset Card for ReCaRe — A Bilingual Legal Benchmark for Revision Candidate Retrieval
+# ReCaRe - A Bilingual Legal Benchmark for Revision Candidate Retrieval
 
 <!-- Provide a quick summary of the dataset. -->
 
@@ -103,7 +101,7 @@ 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 a single shared corpus.
+retrieval tasks (Rat2Rev and Rev2Rev) over bilingual corpora.
 
 ## Dataset Details
 
@@ -118,33 +116,18 @@ 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 formalises two complementary RCR tasks over a shared bilingual
-corpus:
+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 one already-revised
-  article, retrieve the other articles revised in the same legislative
-  event (co-revised articles).
-
-The dataset is the resource artifact of the CIKM 2026 paper
-*"ReCaRe: A Bilingual Legal Benchmark for Revision Candidate Retrieval."*
+- **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:** Hiroyoshi Itō, Yūma Kurokawa, Makoto P. Kato (University of Tsukuba); Sumio Fujita (LY Corporation).
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** `kasys` (HuggingFace organization); maintained by `mpkato`.
-- **Language(s) (NLP):** English (`en`, EU subset) and Japanese (`ja`, Japanese subset).
+- **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).
 
-### Dataset Sources [optional]
-
-<!-- Provide the basic links for the dataset. -->
-
-- **Repository:** <https://huggingface.co/datasets/kasys/ReCaRe>
-- **Paper [optional]:** Itō, Kurokawa, Kato & Fujita. *ReCaRe: A Bilingual Legal Benchmark for Revision Candidate Retrieval.* CIKM 2026 Resource Track (under submission).
-- **Demo [optional]:** [More Information Needed]
-
 ## Uses
 
 <!-- Address questions around how the dataset is intended to be used. -->
@@ -155,9 +138,8 @@ The dataset is the resource artifact of the CIKM 2026 paper
 
 ReCaRe is intended as a research benchmark for:
 
-- Training and evaluating multilingual / cross-lingual retrieval models on
-  legal text where queries and target documents differ markedly in length
-  and register.
+- 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.
@@ -182,15 +164,14 @@ ReCaRe is **not** designed for, and should not be used as, a basis for:
   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).
+  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:
+The dataset is organised as **12 configs** sharing two language-aligned corpora:
 
 | Config | Splits | Schema | Records |
 | --- | --- | --- | --- |
@@ -218,15 +199,13 @@ without adapter code.
 `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
+`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
-inherited from the prior DEIM 2026 papers (Itō et al. 2F-01; Kurokawa et
-al. 4F-04) for direct comparability with their reported numbers. The
-`corpus-*`, `queries-*`, and `metadata-*` configs are not split — the full
+**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.
 
@@ -251,7 +230,7 @@ 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 a single legal corpus, in two jurisdictions whose
+(Rat2Rev, Rev2Rev) over legal corpora, in two jurisdictions whose
 legal-revision practices differ (EU consolidation tradition vs. Japanese
 amending-act tradition).
 
@@ -261,22 +240,19 @@ amending-act tradition).
 
 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).
+- **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).
+  / 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-Q3–2026-Q1. Full consolidated text was downloaded
+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 through **Dice /
-Simpson coefficient matching** between paired article texts; **minor-edit
-filtering** (punctuation-only and pure renumbering) removed alignments
-without semantic change.
+were then aligned with their before/after revisions.
 
 Queries are derived deterministically from this alignment:
 
@@ -290,10 +266,6 @@ 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.
 
-The construction pipeline depends on internal scrapers and is not part of
-the public release, but the resulting dataset is fully public so that
-retrieval-side experiments are completely reproducible.
-
 #### 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. -->
@@ -318,28 +290,6 @@ Relevance labels (qrels) are produced **automatically** from the official
 amendment alignment described above; there is no per-pair human labelling
 in the released qrels.
 
-A separate **label-validity audit** is conducted on a stratified sample of
-200 query–document pairs (50 per `(task, language)` slice; per-slice
-strata: 25 positive, 12 hard-negative drawn from BM25 top-100 minus
-positives, 13 random-negative drawn from the corpus minus positives), to
-verify that the construction pipeline's labels agree with human judgement.
-Two annotators independently judge each pair as `relevant` /
-`not relevant` under blind conditions (provenance and stratum hidden in a
-custom web tool). Reported metrics include positive precision (P_pos),
-hard-negative precision (P_neg-hard), random-negative precision
-(P_neg-rand), and Cohen's κ for inter-annotator agreement. Audit results
-appear in the paper; the audit sample is **not** part of the released qrels.
-
-#### Who are the annotators?
-
-<!-- This section describes the people or systems who created the annotations. -->
-
-The label-validity audit annotators are the paper's first authors
-(H. Itō and Y. Kurokawa). Both have research experience in legal
-information retrieval (DEIM 2026 papers 2F-01 and 4F-04). They are also
-authors of the construction pipeline, which is acknowledged as an
-annotator-construction overlap in the paper's Limitations.
-
 #### 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. -->
@@ -348,37 +298,12 @@ 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. The dataset does not contain personal contact
-information, financial information, health information, or any data that
-would qualify as personal or sensitive under GDPR / Japanese personal
-information protection law.
-
-The label-validity audit (above) is performed by the paper's co-authors on
-already-public legal articles, with no external participants. The work is
-**not subject to IRB review** under the standard "human subjects research"
-definition.
+have not been anonymised. 
 
 ## Bias, Risks, and Limitations
 
 <!-- This section is meant to convey both technical and sociotechnical limitations. -->
 
-- **Annotator-construction overlap.** The label-validity audit is performed
-  by authors of the construction pipeline; despite blind judgement, this
-  raises the possibility of subtle bias toward the pipeline's decisions.
-  Third-party annotation is left as future work.
-- **Coverage windows differ.** EU period is 2010–2025; Japanese period is
-  2019–2025. The longer EU window is a function of upstream availability
-  and means temporal generalisation conclusions across the two subsets
-  must be drawn with care.
-- **Construction code not released.** The alignment pipeline relies on
-  internal scrapers (EUR-Lex / e-Gov) that are not part of the release.
-  Independent verification of the relevance signal therefore depends on
-  the documentation in the paper rather than re-runnable construction
-  code; the dataset itself, however, is fully public so retrieval
-  experiments are completely reproducible.
-- **Implicit dependency in Rev2Rev.** Some co-revised article pairs share
-  little surface vocabulary; this is the intended difficulty of the task,
-  but lexical retrievers (e.g. BM25) systematically underperform.
 - **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
@@ -386,76 +311,16 @@ definition.
 - **Not a substitute for legal advice.** The dataset is a research
   resource. Retrieved articles are revision *candidates* for expert review.
 
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users should:
-
-- Treat the dataset as an evaluation benchmark for retrieval research, not
-  as a training signal for production legal-decision systems.
-- Report results separately by language and task; cross-language averaging
-  can hide systematic differences.
-- Cite both the dataset (this HuggingFace artifact) and the CIKM 2026
-  paper, and acknowledge the upstream public-source licenses (EUR-Lex,
-  e-Gov / 法令索引 / 衆議院議案).
-- Expect that strong off-the-shelf retrievers will show substantial
-  headroom on both tasks; the paper documents this as a feature, not a
-  defect, of the resource.
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-```bibtex
-@inproceedings{ito2026recare,
-  title     = {{ReCaRe}: A Bilingual Legal Benchmark for Revision Candidate Retrieval},
-  author    = {It\={o}, Hiroyoshi and Kurokawa, Y\={u}ma and Kato, Makoto P. and Fujita, Sumio},
-  booktitle = {Proceedings of the 35th ACM International Conference on Information and Knowledge Management},
-  series    = {CIKM '26},
-  year      = {2026},
-  publisher = {Association for Computing Machinery},
-  note      = {Resource Track. Dataset: \url{https://huggingface.co/datasets/kasys/ReCaRe}},
-}
-```
-
-**APA:**
-
-> Itō, H., Kurokawa, Y., Kato, M. P., & Fujita, S. (2026). *ReCaRe: A Bilingual Legal Benchmark for Revision Candidate Retrieval.* In Proceedings of the 35th ACM International Conference on Information and Knowledge Management (CIKM '26). [https://huggingface.co/datasets/kasys/ReCaRe](https://huggingface.co/datasets/kasys/ReCaRe)
-
-(DOI to be added once HF Settings → Generate DOI is run on tag `v0.1.0`.)
-
 ## 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 umbrella retrieval task of
+- **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 one revised article,
+- **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.
-- **CELEX number.** The unique identifier used by EUR-Lex for EU legal
-  documents.
-
-## More Information [optional]
-
-- **Versioning.** This release is `v0.1.0`; corrective releases will use
-  `v0.1.x` (patch) or `v0.x.0` (minor) and be tagged on this HuggingFace
-  repository. A Zenodo mirror is planned for additional persistence.
-- **Codebase.** Analysis and baseline reproduction code lives at the
-  `kasys-lab/ReCaRe` GitHub repository. The dataset construction pipeline
-  is intentionally not released (relies on internal scrapers).
-
-## Dataset Card Authors [optional]
-
-Hiroyoshi Itō, Yūma Kurokawa, Makoto P. Kato (University of Tsukuba); Sumio Fujita (LY Corporation).
-
-## Dataset Card Contact
-
-Makoto P. Kato — `mpkato@slis.tsukuba.ac.jp`
+  or domestic amending law) that modifies one or more prior provisions.