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--- |
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license: cc-by-2.0 |
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task_categories: |
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- text-classification |
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language: |
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- en |
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size_categories: |
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- 100K<n<1M |
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--- |
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# PLANE Out-of-Distribution Sets |
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PLANE (phrase-level adjective-noun entailment) is a benchmark to test models on fine-grained compositional inference. |
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The current dataset contains five sampled splits, used in the supervised experiments of [Bertolini et al., 22](https://aclanthology.org/2022.coling-1.359/). |
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## Data Structure |
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The `dataset` is organised around five `Train/test_split#`, each containing a training and test set of circa 60K and 2K. |
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### Features |
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Each entrance has 6 features: `seq, label, Adj_Class, Adj, Nn, Hy` |
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- `seq`:test sequense |
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- `label`: ground truth (1:entialment, 0:no-entailment) |
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- `Adj_Class`: the class of the sequence adjectives |
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- `Adj`: the adjective of the sequence (I: intersective, S: subsective, O: intensional) |
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- `N`n: the noun |
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- `Hy`: the noun's hypericum |
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Each sample in `seq` can take one of three forms (or inference types, in paper): |
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- An *Adjective-Noun* is a *Noun* (e.g. A red car is a car) |
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- An *Adjective-Noun* is a *Hypernym(Noun)* (e.g. A red car is a vehicle) |
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- An *Adjective-Noun* is a *Adjective-Hypernym(Noun)* (e.g. A red car is a red vehicle) |
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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. |
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### Trained Model |
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You can find a tuned BERT-base model (tuned and validated using the 2nd split) [here](https://huggingface.co/lorenzoscottb/bert-base-cased-PLANE-ood-2?text=A+fake+smile+is+a+smile). |
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### Cite |
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If you use PLANE for your work, please cite the main COLING 2022 paper. |
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``` |
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@inproceedings{bertolini-etal-2022-testing, |
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title = "Testing Large Language Models on Compositionality and Inference with Phrase-Level Adjective-Noun Entailment", |
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author = "Bertolini, Lorenzo and |
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Weeds, Julie and |
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Weir, David", |
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booktitle = "Proceedings of the 29th International Conference on Computational Linguistics", |
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month = oct, |
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year = "2022", |
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address = "Gyeongju, Republic of Korea", |
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publisher = "International Committee on Computational Linguistics", |
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url = "https://aclanthology.org/2022.coling-1.359", |
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pages = "4084--4100", |
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} |
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``` |
