KLAR - reproducibility bundle

Data and code to reproduce the paper Alles klar? A Rule-Based Simplicity Score for German Text Simplification (KlarText workshop; proceedings link to follow). KLAR is a reference-free, rule-based readability score for German plain language. This repository reproduces every table and number in the paper.

The bundle ships text-free per-document scores (numeric features only, no source or simplified text) plus the analysis code that turns them into the paper's results. The scoring engine that produced the numbers is a closed product and is not included: you can re-run the statistics on the released scores, but you cannot regenerate the scores themselves.

Some of the underlying corpora are license-restricted, so their texts are not redistributed here. If you hold a license and want to see the text behind a score, docs/DATASETS.md explains how to rebuild each corpus into data/.

Quickstart

python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt

python -m experiments.rq1_validate --scores scores/deplain_web.jsonl
python -m experiments.rq2_convergent --scores-dir scores
python -m experiments.rq3_graded --scores scores/apa_lha.jsonl
python -m experiments.rq4_competitors
python -m experiments.rq4_tost

Each script writes its results JSON (and, for RQ2, a scatter figure) to results/ and prints a report to stdout. See docs/REPRODUCE.md for the full command set across all four corpora and the mapping from each script to its research question.

Layout

• scores/: the released text-free per-document score bundles (JSONL), one file per corpus.
• experiments/: the analysis scripts. They read only the scores; the scorer is not included.
• data/: empty. License holders rebuild corpora here (see docs/DATASETS.md).
• docs/DATASETS.md: how to obtain and rebuild each corpus.
• docs/REPRODUCE.md: full reproduction walkthrough.

Dataset structure

Each file in scores/ is JSONL, one row per document. Every evaluated pair (an original document and its simplified counterpart) contributes two rows, linked by pair_idx and distinguished by side (orig / simp). Rows carry numbers and opaque metadata only: no source or simplified text is present anywhere in the bundle.

Fields (see experiments/scores_bundle.py::make_row, the source of truth):

• schema: bundle schema version (currently 1).
• item_id: "<dataset>:<pair_idx>:<side>", e.g. "apa_lha:0:orig".
• dataset: corpus name (matches the file, e.g. "apa_lha").
• pair_idx: index of the orig/simp pair within the corpus.
• side: "orig" or "simp".
• sub: source sub-corpus label (feeds corpus filtering and per-subcorpus stats).
• register: source register label, or null.
• per_rule: {rule_name: {raw, scaled, w}} for the 20 calibrated readability rules (e.g. rule_simple_words, rule_short_sentences). raw is the unscaled rule score, scaled maps it onto a common 0-1 scale, w is the rule's calibrated weight.
• composite: {raw, scaled, scaled_conf}. scaled_conf is the metric reported in the paper.
• readability: {flesch, lix, wiener_sachtextformel}, the standard readability formulas computed on the source text, sign-corrected so that higher always means simpler.
• n_words: word count of the (unreleased) source text.
• meta: non-text scalar fields carried through from the source corpus (e.g. level, article_id); never free text.

Example row (fabricated numbers):

{
  "schema": 1,
  "item_id": "apa_lha:0:orig",
  "dataset": "apa_lha",
  "pair_idx": 0,
  "side": "orig",
  "sub": "apa_lha_a2",
  "register": null,
  "per_rule": {
    "rule_simple_words": { "raw": 0.71, "scaled": 0.64, "w": 0.61 }
  },
  "composite": { "raw": 0.77, "scaled": 0.47, "scaled_conf": 0.38 },
  "readability": { "flesch": 55.9, "lix": -58.2, "wiener_sachtextformel": -10.0 },
  "n_words": 288,
  "meta": { "level": "A2", "article_id": "0_2019" }
}

scores/competitors.jsonl is a second, differently-shaped bundle for the RQ4 competitor comparison: one row per (dataset, item, system) instead of per (dataset, item, side). system is one of source / human / KLAR or a competitor model (e.g. German4all, erlesen-leo-7b, erlesen-leo-13b, elgepa-8b). Fields: composite.scaled_conf, readability.flesch, n_words, sub; no per-rule breakdown, no text. Read by experiments/rq4_competitors.py and rq4_tost.py.

Citation

TODO: add the BibTeX once the proceedings are out. Until then, cite the paper by title: Alles klar? A Rule-Based Simplicity Score for German Text Simplification (KlarText workshop).