Datasets:

nyu-mll
/

blimp

Tasks:

Text Classification

Modalities:

Formats:

Sub-tasks:

acceptability-classification

Languages:

Size:

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License:

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Dataset Card for "blimp"

Dataset Summary

BLiMP is a challenge set for evaluating what language models (LMs) know about major grammatical phenomena in English. BLiMP consists of 67 sub-datasets, each containing 1000 minimal pairs isolating specific contrasts in syntax, morphology, or semantics. The data is automatically generated according to expert-crafted grammars.

Supported Tasks and Leaderboards

More Information Needed

Languages

More Information Needed

Dataset Structure

Data Instances

adjunct_island

Size of downloaded dataset files: 0.36 MB
Size of the generated dataset: 0.17 MB
Total amount of disk used: 0.52 MB

An example of 'train' looks as follows.

{
    "UID": "tough_vs_raising_1",
    "field": "syntax_semantics",
    "lexically_identical": false,
    "linguistics_term": "control_raising",
    "one_prefix_method": false,
    "pair_id": 2,
    "sentence_bad": "Benjamin's tutor was certain to boast about.",
    "sentence_good": "Benjamin's tutor was easy to boast about.",
    "simple_LM_method": true,
    "two_prefix_method": false
}

anaphor_gender_agreement

Size of downloaded dataset files: 0.44 MB
Size of the generated dataset: 0.14 MB
Total amount of disk used: 0.57 MB

An example of 'train' looks as follows.

{
    "UID": "tough_vs_raising_1",
    "field": "syntax_semantics",
    "lexically_identical": false,
    "linguistics_term": "control_raising",
    "one_prefix_method": false,
    "pair_id": 2,
    "sentence_bad": "Benjamin's tutor was certain to boast about.",
    "sentence_good": "Benjamin's tutor was easy to boast about.",
    "simple_LM_method": true,
    "two_prefix_method": false
}

anaphor_number_agreement

Size of downloaded dataset files: 0.45 MB
Size of the generated dataset: 0.14 MB
Total amount of disk used: 0.59 MB

An example of 'train' looks as follows.

{
    "UID": "tough_vs_raising_1",
    "field": "syntax_semantics",
    "lexically_identical": false,
    "linguistics_term": "control_raising",
    "one_prefix_method": false,
    "pair_id": 2,
    "sentence_bad": "Benjamin's tutor was certain to boast about.",
    "sentence_good": "Benjamin's tutor was easy to boast about.",
    "simple_LM_method": true,
    "two_prefix_method": false
}

animate_subject_passive

Size of downloaded dataset files: 0.46 MB
Size of the generated dataset: 0.15 MB
Total amount of disk used: 0.61 MB

An example of 'train' looks as follows.

{
    "UID": "tough_vs_raising_1",
    "field": "syntax_semantics",
    "lexically_identical": false,
    "linguistics_term": "control_raising",
    "one_prefix_method": false,
    "pair_id": 2,
    "sentence_bad": "Benjamin's tutor was certain to boast about.",
    "sentence_good": "Benjamin's tutor was easy to boast about.",
    "simple_LM_method": true,
    "two_prefix_method": false
}

animate_subject_trans

Size of downloaded dataset files: 0.43 MB
Size of the generated dataset: 0.13 MB
Total amount of disk used: 0.57 MB

An example of 'train' looks as follows.

{
    "UID": "tough_vs_raising_1",
    "field": "syntax_semantics",
    "lexically_identical": false,
    "linguistics_term": "control_raising",
    "one_prefix_method": false,
    "pair_id": 2,
    "sentence_bad": "Benjamin's tutor was certain to boast about.",
    "sentence_good": "Benjamin's tutor was easy to boast about.",
    "simple_LM_method": true,
    "two_prefix_method": false
}

Data Fields

The data fields are the same among all splits.

adjunct_island

sentence_good: a string feature.
sentence_bad: a string feature.
field: a string feature.
linguistics_term: a string feature.
UID: a string feature.
simple_LM_method: a bool feature.
one_prefix_method: a bool feature.
two_prefix_method: a bool feature.
lexically_identical: a bool feature.
pair_id: a int32 feature.

anaphor_gender_agreement

sentence_good: a string feature.
sentence_bad: a string feature.
field: a string feature.
linguistics_term: a string feature.
UID: a string feature.
simple_LM_method: a bool feature.
one_prefix_method: a bool feature.
two_prefix_method: a bool feature.
lexically_identical: a bool feature.
pair_id: a int32 feature.

anaphor_number_agreement

sentence_good: a string feature.
sentence_bad: a string feature.
field: a string feature.
linguistics_term: a string feature.
UID: a string feature.
simple_LM_method: a bool feature.
one_prefix_method: a bool feature.
two_prefix_method: a bool feature.
lexically_identical: a bool feature.
pair_id: a int32 feature.

animate_subject_passive

sentence_good: a string feature.
sentence_bad: a string feature.
field: a string feature.
linguistics_term: a string feature.
UID: a string feature.
simple_LM_method: a bool feature.
one_prefix_method: a bool feature.
two_prefix_method: a bool feature.
lexically_identical: a bool feature.
pair_id: a int32 feature.

animate_subject_trans

sentence_good: a string feature.
sentence_bad: a string feature.
field: a string feature.
linguistics_term: a string feature.
UID: a string feature.
simple_LM_method: a bool feature.
one_prefix_method: a bool feature.
two_prefix_method: a bool feature.
lexically_identical: a bool feature.
pair_id: a int32 feature.

Data Splits

name	train
adjunct_island	1000
anaphor_gender_agreement	1000
anaphor_number_agreement	1000
animate_subject_passive	1000
animate_subject_trans	1000

Dataset Creation

Curation Rationale

More Information Needed

Source Data

Initial Data Collection and Normalization

More Information Needed

Who are the source language producers?

More Information Needed

Annotations

Annotation process

More Information Needed

Who are the annotators?

More Information Needed

Personal and Sensitive Information

More Information Needed

Considerations for Using the Data

Social Impact of Dataset

More Information Needed

Discussion of Biases

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Other Known Limitations

More Information Needed

Additional Information

Dataset Curators

More Information Needed

Licensing Information

BLiMP is distributed under a CC-BY license. Source: https://github.com/alexwarstadt/blimp#license

Citation Information

@article{warstadt2020blimp,
    author = {Warstadt, Alex and Parrish, Alicia and Liu, Haokun and Mohananey, Anhad and Peng, Wei and Wang, Sheng-Fu and Bowman, Samuel R.},
    title = {BLiMP: The Benchmark of Linguistic Minimal Pairs for English},
    journal = {Transactions of the Association for Computational Linguistics},
    volume = {8},
    number = {},
    pages = {377-392},
    year = {2020},
    doi = {10.1162/tacl\_a\_00321},
    URL = {https://doi.org/10.1162/tacl_a_00321},
    eprint = {https://doi.org/10.1162/tacl_a_00321},
    abstract = { We introduce The Benchmark of Linguistic Minimal Pairs (BLiMP),1 a challenge set for evaluating the linguistic knowledge of language models (LMs) on major grammatical phenomena in English. BLiMP consists of 67 individual datasets, each containing 1,000 minimal pairs—that is, pairs of minimally different sentences that contrast in grammatical acceptability and isolate specific phenomenon in syntax, morphology, or semantics. We generate the data according to linguist-crafted grammar templates, and human aggregate agreement with the labels is 96.4\%. We evaluate n-gram, LSTM, and Transformer (GPT-2 and Transformer-XL) LMs by observing whether they assign a higher probability to the acceptable sentence in each minimal pair. We find that state-of-the-art models identify morphological contrasts related to agreement reliably, but they struggle with some subtle semantic and syntactic phenomena, such as negative polarity items and extraction islands. }
}

Errata

Some results were misreported in the published TACL version. Please refer to the corrected version on arXiv: https://arxiv.org/abs/1912.00582

Contributions

Thanks to @lhoestq, @patrickvonplaten, @thomwolf for adding this dataset.

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BLiMP: The Benchmark of Linguistic Minimal Pairs for English

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Paper for nyu-mll/blimp

BLiMP: The Benchmark of Linguistic Minimal Pairs for English

Paper • 1912.00582 • Published Dec 2, 2019