Update README.md

README.md

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 ---
-license: apache-2.0
+language:
+- en
+license: cc-by-sa-4.0
+task_categories:
+- feature-extraction
+- text-classification
+size_categories:
+- 10K<n<100K
+tags:
+- naturalness
+- fluency
+- ordinal-regression
+- contrastive-learning
+- machine-translation-evaluation
+pretty_name: Naturalness Spectrum
+source_datasets:
+- extended|wmt06
+- extended|wi-locness
+- extended|onestop-english
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train.parquet
+  - split: test
+    path: data/test.parquet
 ---
+
+# Naturalness Spectrum
+
+An 8-level ordinal dataset for English-text **naturalness**: the degree to which a text conforms to the distributional and stylistic regularities of fluent, idiomatic native English, independent of its semantic content. Spans the spectrum from severely disfluent statistical-MT output, through learner English at three CEFR proficiency bands, to professionally edited native prose at three reading levels.
+
+To our knowledge, this is the first dataset explicitly labelled for naturalness as a continuous ordinal property. It is constructed by composing three existing corpora whose texts can be ordered by provenance, rather than by collecting fresh human ratings — no new human annotation was solicited.
+
+## Dataset summary
+
+| | |
+|---|---|
+| **Size** | 65,868 sentences (52,694 train / 13,174 test, stratified 80/20) |
+| **Label space** | Integer 0–7, ordinal (higher = more natural) |
+| **Sentence length** | 5–60 words (filtered) |
+| **Language** | English |
+| **Label provenance** | By source corpus (no per-sentence annotation) |
+| **Constituent corpora** | WMT06, W&I+LOCNESS, OneStopEnglish |
+
+### The 8-level spectrum
+
+| Label | Source | Description | Train |
+|:---:|---|---|---:|
+| 0 | WMT06 (human fluency scores 1–2) | Very disfluent MT output | 1,947 |
+| 1 | WMT06 (human fluency score 3) | Mediocre MT output | 2,560 |
+| 2 | W&I CEFR A | Beginner learner English | 6,990 |
+| 3 | W&I CEFR B | Intermediate learner English | 9,099 |
+| 4 | W&I CEFR C | Advanced learner English | 7,786 |
+| 5 | OneStopEnglish Elementary | Simplified native English | 8,102 |
+| 6 | OneStopEnglish Intermediate | Standard native English | 8,195 |
+| 7 | OneStopEnglish Advanced | Sophisticated native English | 8,015 |
+
+The spectrum groups naturally into three macro-bands — MT (0–1), Learner (2–4), Native (5–7) — and the ordering within each band is established by construction from the source labels.
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("foudilir/naturalness-spectrum")
+print(ds)
+# DatasetDict({
+#     train: Dataset({features: ['text', 'label', 'source'], num_rows: 52694}),
+#     test:  Dataset({features: ['text', 'label', 'source'], num_rows: 13174}),
+# })
+
+print(ds["train"][0])
+# {'text': '...', 'label': 6, 'source': 'OSE_Intermediate'}
+```
+
+### Data fields
+
+| Field | Type | Description |
+|---|---|---|
+| `text` | `string` | English sentences |
+| `label` | `int8` | Ordinal naturalness label, 0–7 |
+| `source` | `string` | Source corpus tag (e.g. `WMT06_1-2`, `WI_CEFR_B`, `OSE_Advanced`) |
+
+### Splits
+
+| Split | Sentences |
+|---|---:|
+| `train` | 52,694 |
+| `test` | 13,174 |
+
+The split is stratified across the 8 labels (80/20). The test split was held out from all model training and hyperparameter selection.
+
+## Dataset creation
+
+### Curation rationale
+
+Existing approaches to text naturalness fall into two camps, each with limitations:
+
+- **MT-evaluation metrics** (COMET, BLEURT) conflate naturalness with adequacy and require a reference translation, so they cannot score isolated text.
+- **Language-model perplexity** is a one-dimensional proxy that rewards low-entropy text independent of distributional match, and inherits the bias of its training corpus.
+
+No corpus provides continuous naturalness ratings, and no off-the-shelf encoder produces naturalness-aware representations. The key construction insight is that while no single corpus provides graded naturalness ratings, several corpora exist whose texts can be ordered by provenance: machine-translated output is, by definition, less natural than text written by native speakers; CEFR-graded learner essays sit between these extremes; reading-level rewrites of the same native articles preserve content while varying surface form.
+
+### Source data
+
+**WMT06 (labels 0–1).** Human fluency judgements from the WMT 2006 shared task on machine-translation evaluation. We restrict to English-target language pairs (DE→EN, ES→EN, FR→EN). MT output was rated on a 1–5 scale; we retain only outputs with scores ≤ 3 (the disfluent end). Sentences with score ≤ 2 are assigned label 0; sentences with score 3 are assigned label 1.
+
+**W&I+LOCNESS (labels 2–4).** The Writing & Improvements corpus from the BEA-2019 shared task: learner essays written by non-native English speakers at CEFR proficiency levels A (beginner), B (intermediate), and C (advanced). Original (pre-correction) text is used, since the learner errors — inconsistent tense, missing articles, non-idiomatic phrasing — constitute the naturalness signal of interest.
+
+**OneStopEnglish (labels 5–7).** The OneStopEnglish corpus: parallel rewrites of the same news articles at three reading levels (Elementary, Intermediate, Advanced). The Advanced level approximates the original adult-directed native article; Elementary and Intermediate are simplifications produced by skilled editors for language learners. All three are produced by native speakers and remain grammatical; the ordering reflects their derivational relationship from the Advanced source, not a quality judgement.
+
+### Annotations
+
+**No per-sentence annotation was solicited.** Labels are assigned by source provenance, following the rationale above. Within-corpus labels (WMT06 fluency scores, CEFR proficiency tags, OSE reading levels) are inherited from the source datasets.
+
+WMT06 fluency scores are inherently noisy: on the 999 multiply-annotated sentences (5.6 % of WMT06 data; avg. 2.07 annotations/sentence), exact inter-annotator agreement ranges from 62.1 % (score 1) to 66.9 % (score 3). The ordinal framework partially absorbs this noise via distance-weighted training objectives.
+
+### Preprocessing
+
+- Sentence tokenisation (NLTK) applied uniformly to all sources, so that OneStopEnglish (whose raw entries are paragraphs or articles) is brought to the same granularity as WMT06 and W&I (already sentence-level).
+- Length filter: sentences of 5–60 words retained. This removes 1,781 sentences (2.6 %) and substantially reduces length confounds across sources.
+- Stratified 80/20 train/test split on the 8-level label.
+
+## Considerations for using the data
+
+### Confound audit
+
+Because the spectrum is assembled from heterogeneous corpora, a model could in principle achieve high ordinal correlation by classifying *source* rather than naturalness. Three of the strongest candidate shortcuts have been audited (see article for details):
+
+| Confound | Probe | Finding |
+|---|---|---|
+| Sentence length | Distribution overlap after filtering | Substantial overlap across all 8 levels |
+| Semantic content | Pairwise cosine in `all-mpnet-base-v2` space, within- vs. cross-source | Within−cross gap ≤ +0.06, distributions overlap heavily |
+| Affective content | Pairwise cosine in a GoEmotions-trained SupCon encoder (`foudil/lens-emotion-encoder`) | Within−cross gap ≤ +0.06, distributions overlap heavily |
+
+This bounds — but does not eliminate — the space of plausible shortcuts. Surface features not measured here (punctuation, capitalisation, MT-pipeline orthographic artefacts) could carry residual signal. Some of these arguably *are* part of the naturalness signal under the working definition; others may be confounds.
+
+### Known limitations and biases
+
+- **English only.** Cross-lingual transfer is not supported by this dataset.
+- **Register coverage.** Spans MT output, classroom learner writing, and graded news prose. Literary, poetic, and rhetorically ornate registers are not represented.
+
+- **Native-band side-signal.** The OneStopEnglish ordering co-varies with lexical and syntactic sophistication. Models trained on this dataset may learn sophistication as a side-signal aligned with high-naturalness scores. This conflates simple-but-natural prose with simplification artefacts.
+- **MT label noise.** WMT06 fluency judgements have moderate inter-annotator agreement (~62–67 %). The MT/Mediocre-MT boundary is noisier than the Learner and Native boundaries.
+- **Non-uniform spacing.** The integer label scale assigns unit distance between all adjacent steps, but actual naturalness gaps are much larger between macro-bands (MT/Learner/Native) than within. Models recover this empirically; raw label arithmetic on the integer scale should be avoided.
+- **Demographic representation.** Inherits the demographic skew of the source corpora (W&I learners are predominantly European; OneStopEnglish is UK-focused news; WMT06 is European-language news translation).
+- **Reddit, social-media, and dialectal English are absent.** The spectrum does not cover informal native registers.
+
+### Personal and sensitive information
+
+The constituent corpora are publicly released for research. No new personal information is introduced. W&I+LOCNESS learner essays were collected under the original BEA-2019 shared-task consent; no demographic identifiers beyond CEFR proficiency are retained here.
+
+## Additional information
+
+### Source data licences and attribution
+
+This dataset re-uses material derived from three publicly available corpora. Each
+source remains governed by its original licence or terms of use. Users must comply
+with the source terms in addition to any terms attached to this aggregated release.
+
+- **WMT06**: Koehn & Monz, “Manual and Automatic Evaluation of Machine Translation between European Languages,” WMT 2006. The WMT06 shared-task page describes the data as publicly available for machine translation and MT evaluation research.
+- **W&I+LOCNESS**: Bryant, Felice, Andersen & Briscoe, “The BEA-2019 Shared Task on Grammatical Error Correction,” BEA 2019. The BEA-2019 release states that W&I+LOCNESS and related corpora are subject to non-commercial-use licence terms.
+- **OneStopEnglish**: Vajjala & Lučić, “OneStopEnglish corpus: A new corpus for automatic readability assessment and text simplification,” BEA 2018. The corpus is released under CC BY-SA 4.0.
+
+Because the constituent corpora have different terms, this aggregated dataset is
+not intended to relicense the source corpora. Downstream users must preserve
+attribution to all three sources and comply with each source’s licence or terms of use.
+
+
+### Citation information
+
+If you use this dataset, please cite both the aggregating article and the three constituent corpora:
+
+```bibtex
+@inproceedings{koehn-monz-2006-manual,
+  title     = {Manual and Automatic Evaluation of Machine Translation between {E}uropean Languages},
+  author    = {Koehn, Philipp and Monz, Christof},
+  booktitle = {Proceedings on the Workshop on Statistical Machine Translation},
+  year      = {2006},
+  month     = jun,
+  address   = {New York City},
+  publisher = {Association for Computational Linguistics},
+  pages     = {102--121},
+  url       = {https://aclanthology.org/W06-3114/}
+}
+
+@inproceedings{bryant-etal-2019-bea,
+  title     = {The {BEA}-2019 Shared Task on Grammatical Error Correction},
+  author    = {Bryant, Christopher and Felice, Mariano and Andersen, {\O}istein E. and Briscoe, Ted},
+  booktitle = {Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications},
+  year      = {2019},
+  month     = aug,
+  address   = {Florence, Italy},
+  publisher = {Association for Computational Linguistics},
+  pages     = {52--75},
+  doi       = {10.18653/v1/W19-4406},
+  url       = {https://aclanthology.org/W19-4406/}
+}
+
+@inproceedings{vajjala-lucic-2018-onestopenglish,
+  title     = {{OneStopEnglish} corpus: A new corpus for automatic readability assessment and text simplification},
+  author    = {Vajjala, Sowmya and Lu{\v{c}}i{\'c}, Ivana},
+  booktitle = {Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications},
+  year      = {2018},
+  month     = jun,
+  address   = {New Orleans, Louisiana},
+  publisher = {Association for Computational Linguistics},
+  pages     = {297--304},
+  doi       = {10.18653/v1/W18-0535},
+  url       = {https://aclanthology.org/W18-0535/}
+}
+```
+
+### Models trained on this dataset
+
+- [`foudil/lens-naturalness-encoder`](https://huggingface.co/foudil/lens-naturalness-encoder) — DeBERTa-v3-base + LoRA, ordinal SupCon fine-tuned, 128-d embedding space where cosine similarity = naturalness proximity.