blimp.py

# coding=utf-8
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# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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# Lint as: python3
"""BLiMP dataset with minimal pairs of grammatical phenomena in English."""


import json

import datasets


_CITATION = """
@article{warstadt2019blimp,
  title={BLiMP: A Benchmark of Linguistic Minimal Pairs for English},
  author={Warstadt, Alex and Parrish, Alicia and Liu, Haokun and Mohananey, Anhad and Peng, Wei, and Wang, Sheng-Fu and Bowman, Samuel R},
  journal={arXiv preprint arXiv:1912.00582},
  year={2019}
}
"""

_DESCRIPTION = """
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.
"""

_PROJECT_URL = "https://github.com/alexwarstadt/blimp/tree/master/"
_DOWNLOAD_URL = "https://raw.githubusercontent.com/alexwarstadt/blimp/master/"


class BlimpConfig(datasets.BuilderConfig):
    """BuilderConfig for Blimp."""

    def __init__(self, name, version=datasets.Version("0.1.0"), **kwargs):
        """BuilderConfig for Blimp.

        Args:
          name (str): UID of the linguistic paradigm
          **kwargs: keyword arguments forwarded to super.
        """
        description = _DESCRIPTION
        description += f"This configuration includes the paradigm {name}."

        super().__init__(name=name, description=description, version=version, **kwargs)


class Blimp(datasets.GeneratorBasedBuilder):
    """Minimal grammatical and ungrammatical pairs of 67 linguistic paradigms."""

    all_paradigms = [
        "adjunct_island",
        "anaphor_gender_agreement",
        "anaphor_number_agreement",
        "animate_subject_passive",
        "animate_subject_trans",
        "causative",
        "complex_NP_island",
        "coordinate_structure_constraint_complex_left_branch",
        "coordinate_structure_constraint_object_extraction",
        "determiner_noun_agreement_1",
        "determiner_noun_agreement_2",
        "determiner_noun_agreement_irregular_1",
        "determiner_noun_agreement_irregular_2",
        "determiner_noun_agreement_with_adj_2",
        "determiner_noun_agreement_with_adj_irregular_1",
        "determiner_noun_agreement_with_adj_irregular_2",
        "determiner_noun_agreement_with_adjective_1",
        "distractor_agreement_relational_noun",
        "distractor_agreement_relative_clause",
        "drop_argument",
        "ellipsis_n_bar_1",
        "ellipsis_n_bar_2",
        "existential_there_object_raising",
        "existential_there_quantifiers_1",
        "existential_there_quantifiers_2",
        "existential_there_subject_raising",
        "expletive_it_object_raising",
        "inchoative",
        "intransitive",
        "irregular_past_participle_adjectives",
        "irregular_past_participle_verbs",
        "irregular_plural_subject_verb_agreement_1",
        "irregular_plural_subject_verb_agreement_2",
        "left_branch_island_echo_question",
        "left_branch_island_simple_question",
        "matrix_question_npi_licensor_present",
        "npi_present_1",
        "npi_present_2",
        "only_npi_licensor_present",
        "only_npi_scope",
        "passive_1",
        "passive_2",
        "principle_A_c_command",
        "principle_A_case_1",
        "principle_A_case_2",
        "principle_A_domain_1",
        "principle_A_domain_2",
        "principle_A_domain_3",
        "principle_A_reconstruction",
        "regular_plural_subject_verb_agreement_1",
        "regular_plural_subject_verb_agreement_2",
        "sentential_negation_npi_licensor_present",
        "sentential_negation_npi_scope",
        "sentential_subject_island",
        "superlative_quantifiers_1",
        "superlative_quantifiers_2",
        "tough_vs_raising_1",
        "tough_vs_raising_2",
        "transitive",
        "wh_island",
        "wh_questions_object_gap",
        "wh_questions_subject_gap",
        "wh_questions_subject_gap_long_distance",
        "wh_vs_that_no_gap",
        "wh_vs_that_no_gap_long_distance",
        "wh_vs_that_with_gap",
        "wh_vs_that_with_gap_long_distance",
    ]

    BUILDER_CONFIGS = [BlimpConfig(paradigm) for paradigm in all_paradigms]

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "sentence_good": datasets.Value("string"),
                    "sentence_bad": datasets.Value("string"),
                    "field": datasets.Value("string"),
                    "linguistics_term": datasets.Value("string"),
                    "UID": datasets.Value("string"),
                    "simple_LM_method": datasets.Value("bool"),
                    "one_prefix_method": datasets.Value("bool"),
                    "two_prefix_method": datasets.Value("bool"),
                    "lexically_identical": datasets.Value("bool"),
                    "pair_id": datasets.Value("int32"),
                }
            ),
            homepage=_PROJECT_URL,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        download_urls = _DOWNLOAD_URL + f"data/{self.config.name}.jsonl"
        downloaded_file = dl_manager.download_and_extract(download_urls)
        return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_file})]

    def _generate_examples(self, filepath):
        """Yields examples."""
        with open(filepath, "r", encoding="utf-8") as f:
            for line in f:
                line_dict = json.loads(line)
                id_ = line_dict["UID"] + "_" + line_dict["pairID"]
                feats = {
                    "sentence_good": line_dict["sentence_good"],
                    "sentence_bad": line_dict["sentence_bad"],
                    "field": line_dict["field"],
                    "linguistics_term": line_dict["linguistics_term"],
                    "UID": line_dict["UID"],
                    "simple_LM_method": line_dict["simple_LM_method"],
                    "one_prefix_method": line_dict["one_prefix_method"],
                    "two_prefix_method": line_dict["two_prefix_method"],
                    "lexically_identical": line_dict["lexically_identical"],
                    "pair_id": int(line_dict["pairID"]),
                }
                yield id_, feats