| --- |
| license: cc-by-2.0 |
| task_categories: |
| - text-classification |
| language: |
| - en |
| size_categories: |
| - 100K<n<1M |
| dataset_info: |
| features: |
| - name: seq |
| dtype: string |
| - name: label |
| dtype: string |
| - name: Adj_Class |
| dtype: string |
| - name: Adj |
| dtype: string |
| - name: Nn |
| dtype: string |
| - name: Hy |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 26047744 |
| num_examples: 300132 |
| - name: test |
| num_bytes: 874524 |
| num_examples: 10080 |
| download_size: 4721262 |
| dataset_size: 26922268 |
| --- |
| |
| Preprocessed from https://huggingface.co/datasets/lorenzoscottb/PLANE-ood/ |
|
|
| ```python |
| df=pd.read_json('https://huggingface.co/datasets/lorenzoscottb/PLANE-ood/resolve/main/PLANE_trntst-OoV_inftype-all.json') |
| f = lambda df: pd.DataFrame(list(zip(*[df[c] for c in df.index])),columns=df.index) |
| ds=DatasetDict() |
| for split in ['train','test']: |
| dfs=pd.concat([f(df[c]) for c in df.columns if split in c.lower()]).reset_index(drop=True) |
| dfs['label']=dfs['label'].map(lambda x:{1:'entailment',0:'not-entailment'}[x]) |
| ds[split]=Dataset.from_pandas(dfs,preserve_index=False) |
| ds.push_to_hub('tasksource/PLANE-ood') |
| ``` |
|
|
|
|
| # PLANE Out-of-Distribution Sets |
|
|
| PLANE (phrase-level adjective-noun entailment) is a benchmark to test models on fine-grained compositional inference. |
| The current dataset contains five sampled splits, used in the supervised experiments of [Bertolini et al., 22](https://aclanthology.org/2022.coling-1.359/). |
|
|
|
|
| ### Features |
|
|
| Each entrance has 6 features: `seq, label, Adj_Class, Adj, Nn, Hy` |
| - `seq`:test sequense |
| - `label`: ground truth (1:entialment, 0:no-entailment) |
| - `Adj_Class`: the class of the sequence adjectives |
| - `Adj`: the adjective of the sequence (I: intersective, S: subsective, O: intensional) |
| - `N`n: the noun |
| - `Hy`: the noun's hypericum |
|
|
| Each sample in `seq` can take one of three forms (or inference types, in paper): |
|
|
| - An *Adjective-Noun* is a *Noun* (e.g. A red car is a car) |
| - An *Adjective-Noun* is a *Hypernym(Noun)* (e.g. A red car is a vehicle) |
| - An *Adjective-Noun* is a *Adjective-Hypernym(Noun)* (e.g. A red car is a red vehicle) |
|
|
| Please note that, as specified in the paper, the ground truth is automatically assigned based on the linguistic rule that governs the interaction between each adjective class and inference type – see the paper for more detail. |
|
|
| ### Cite |
|
|
| If you use PLANE for your work, please cite the main COLING 2022 paper. |
| ``` |
| @inproceedings{bertolini-etal-2022-testing, |
| title = "Testing Large Language Models on Compositionality and Inference with Phrase-Level Adjective-Noun Entailment", |
| author = "Bertolini, Lorenzo and |
| Weeds, Julie and |
| Weir, David", |
| booktitle = "Proceedings of the 29th International Conference on Computational Linguistics", |
| month = oct, |
| year = "2022", |
| address = "Gyeongju, Republic of Korea", |
| publisher = "International Committee on Computational Linguistics", |
| url = "https://aclanthology.org/2022.coling-1.359", |
| pages = "4084--4100", |
| } |
| |
| ``` |
