Train_1 dict	test_1 dict	Train_2 dict	test_2 dict	Train_3 dict	test_3 dict	Train_4 dict	test_4 dict	Train_5 dict	test_5 dict
{"seq":["A soviet assertions is a assertions","A soviet assertions is a declaration","A soviet asser(...TRUNCATED)	{"seq":["A weekly load is a load","A weekly load is a weight","A weekly load is a weekly weight","A (...TRUNCATED)	{"seq":["A polish rifle is a rifle","A polish rifle is a firearm","A polish rifle is a polish firear(...TRUNCATED)	{"seq":["A mexican dealer is a dealer","A mexican dealer is a merchant","A mexican dealer is a mexic(...TRUNCATED)	{"seq":["A irish mac is a mac","A irish mac is a raincoat","A irish mac is a irish raincoat","A basi(...TRUNCATED)	{"seq":["A digital status is a status","A digital status is a state","A digital status is a digital (...TRUNCATED)	{"seq":["A private speakers is a speakers","A private speakers is a articulator","A private speakers(...TRUNCATED)	{"seq":["A democratic security is a security","A democratic security is a safety","A democratic secu(...TRUNCATED)	{"seq":["A chinese representatives is a representatives","A chinese representatives is a negotiator"(...TRUNCATED)	{"seq":["A dead path is a path","A dead path is a course","A dead path is a dead course","A clinical(...TRUNCATED)

Train_1

dict

test_1

dict

Train_2

dict

test_2

dict

Train_3

dict

test_3

dict

Train_4

dict

test_4

dict

Train_5

dict

test_5

dict

{"seq":["A soviet assertions is a assertions","A soviet assertions is a declaration","A soviet asser(...TRUNCATED)

{"seq":["A weekly load is a load","A weekly load is a weight","A weekly load is a weekly weight","A (...TRUNCATED)

{"seq":["A polish rifle is a rifle","A polish rifle is a firearm","A polish rifle is a polish firear(...TRUNCATED)

{"seq":["A mexican dealer is a dealer","A mexican dealer is a merchant","A mexican dealer is a mexic(...TRUNCATED)

{"seq":["A irish mac is a mac","A irish mac is a raincoat","A irish mac is a irish raincoat","A basi(...TRUNCATED)

{"seq":["A digital status is a status","A digital status is a state","A digital status is a digital (...TRUNCATED)

{"seq":["A private speakers is a speakers","A private speakers is a articulator","A private speakers(...TRUNCATED)

{"seq":["A democratic security is a security","A democratic security is a safety","A democratic secu(...TRUNCATED)

{"seq":["A chinese representatives is a representatives","A chinese representatives is a negotiator"(...TRUNCATED)

{"seq":["A dead path is a path","A dead path is a course","A dead path is a dead course","A clinical(...TRUNCATED)

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.

Data Structure

The dataset is organised around five Train/test_split#, each containing a training and test set of circa 60K and 2K.

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)
Nn: 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.

Trained Model

You can find a tuned BERT-base model (tuned and validated using the 2nd split) here.

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",
}

Datasets:

lorenzoscottb
/

PLANE-ood

PLANE Out-of-Distribution Sets

Data Structure

Features

Trained Model

Cite

Models trained or fine-tuned on lorenzoscottb/PLANE-ood

lorenzoscottb/bert-base-cased-PLANE-ood-2