--- license: cc-by-4.0 language: - de task_categories: - text-classification tags: - legal - statutory-interpretation - german-law - argument-mining pretty_name: Interpretive Canons (instance-level) configs: - config_name: argument data_files: - split: train path: arguments/argument/train.json - split: validation path: arguments/argument/validation.json - split: test path: arguments/argument/test.json - config_name: wortlaut data_files: - split: train path: arguments/wortlaut/train.json - split: validation path: arguments/wortlaut/validation.json - split: test path: arguments/wortlaut/test.json - config_name: systematik data_files: - split: train path: arguments/systematik/train.json - split: validation path: arguments/systematik/validation.json - split: test path: arguments/systematik/test.json - config_name: geschichte data_files: - split: train path: arguments/geschichte/train.json - split: validation path: arguments/geschichte/validation.json - split: test path: arguments/geschichte/test.json - config_name: zweck data_files: - split: train path: arguments/zweck/train.json - split: validation path: arguments/zweck/validation.json - split: test path: arguments/zweck/test.json - config_name: nicht_abstrakt data_files: - split: train path: deutungen/nicht_abstrakt/train.json - split: validation path: deutungen/nicht_abstrakt/validation.json - split: test path: deutungen/nicht_abstrakt/test.json - config_name: nicht_selbst_aufgestellt data_files: - split: train path: deutungen/nicht_selbst_aufgestellt/train.json - split: validation path: deutungen/nicht_selbst_aufgestellt/validation.json - split: test path: deutungen/nicht_selbst_aufgestellt/test.json - config_name: konkretes_gesetz data_files: - split: train path: deutungen/konkretes_gesetz/train.json - split: validation path: deutungen/konkretes_gesetz/validation.json - split: test path: deutungen/konkretes_gesetz/test.json --- # Interpretive Canons — instance-level Companion benchmark to the paper *Classifying Interpretive Canons at the Sentence Level: A Benchmark from the German Federal Constitutional Court*. This is the **instance-level** release: one record per (subtask, candidate) pair, grouped into eight subtasks, each with decision-disjoint train/validation/test splits (no decision appears in more than one split). The companion **decision-level** release (one record per exhaustively annotated decision, for end-to-end pipeline evaluation) is at `felix453/interpretive-canons-decisions`. Gold labels come from a single expert annotator. Text is German (Bundesverfassungsgericht decisions). ## Subtasks and split sizes The `arguments` family classifies (reading, candidate-sentence) pairs; the `deutungen` family classifies single reading sentences. | Subtask | Train | Validation | Test | |---|---|---|---| | `argument` | 40 | 40 | 120 | | `wortlaut` | 40 | 40 | 120 | | `systematik` | 40 | 40 | 120 | | `geschichte` | 40 | 40 | 120 | | `zweck` | 40 | 40 | 120 | | `nicht_abstrakt` | 80 | 80 | 240 | | `nicht_selbst_aufgestellt` | 80 | 80 | 240 | | `konkretes_gesetz` | 80 | 80 | 240 | ## Record format Each record is a JSON object `{"id", "row", "observed"}`. `row` holds the sentence(s) and the surrounding context, with the classified sentence marked by inline XML tags (`...` for readings, `...` for argument candidates) inside `text_with_context`. `observed` holds the gold labels for the subtask: - `observed.argument` (0/1): does the candidate sentence justify the reading at all? - `observed.wortlaut` (0/1): grammatical-canon argument? - `observed.systematik` (0/1): systematic-canon argument? - `observed.geschichte` (0/1): historical-canon argument? - `observed.zweck` (0/1): objective-teleological-canon argument? - `observed.nicht_abstrakt` (0/1): the statement is *not* abstract (concrete/case-bound). - `observed.nicht_selbst_aufgestellt` (0/1): the statement is *not* self-advanced (cites only precedent). - `observed.konkretes_gesetz` (list of strings): the concrete statutory provision(s) the reading interprets; empty list means none. `observed.reasoning` is the annotator's free-text rationale where recorded. ## Loading ```python from datasets import load_dataset ds = load_dataset("felix453/interpretive-canons-instances", "wortlaut") print(ds["test"][0]) ``` ## Raw annotations (provenance) The raw, pre-merge Label Studio export these data are built from is available at [`felix453/interpretive-canons-raw-export`](https://huggingface.co/datasets/felix453/interpretive-canons-raw-export). It retains two fields the postprocessing merge does not preserve: the per-reading determinate-content judgment `bestimmt_moeglicheDeutung` (dropped by the merge) and the un-flattened list of concrete provisions (the merge joins the entries into one unsplittable string). Join by Label Studio region `id`.