mctest.py

# coding=utf-8
# Copyright 2020 The HuggingFace Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# 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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"""MCTest: Machine comprehension test: http://research.microsoft.com/mct"""


import os
import datasets


_CITATION = """\
@inproceedings{richardson-etal-2013-mctest,
    title = "{MCT}est: A Challenge Dataset for the Open-Domain Machine Comprehension of Text",
    author = "Richardson, Matthew  and
      Burges, Christopher J.C.  and
      Renshaw, Erin",
    booktitle = "Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing",
    month = oct,
    year = "2013",
    address = "Seattle, Washington, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/D13-1020",
    pages = "193--203",
}
"""

_DESCRIPTION = """\
MCTest requires machines to answer multiple-choice reading comprehension questions about fictional stories, directly tackling the high-level goal of open-domain machine comprehension.
"""

_HOMEPAGE = "https://www.aclweb.org/anthology/D13-1020/"

_DATA_URL = "http://parl.ai/downloads/mctest/mctest.tar.gz"


class MCTest(datasets.GeneratorBasedBuilder):
    """MCTest: Machine comprehension test: http://research.microsoft.com/mct"""

    VERSION = datasets.Version("1.0.0")

    BUILDER_CONFIGS = [
        datasets.BuilderConfig(
            name="mc500",
            version=VERSION,
            description="MC 500",
        ),
        datasets.BuilderConfig(
            name="mc160",
            version=VERSION,
            description="MC 160",
        ),
    ]

    DEFAULT_CONFIG_NAME = "mc500"

    def _info(self):
        if self.config.name == "mc500":
            features = datasets.Features(
                {
                    "idx": dict(
                        {"story": datasets.Value("string"),
                         "question": datasets.Value("int32")
                         }
                    ),
                    "question": datasets.Value("string"),
                    "story": datasets.Value("string"),
                    "properties": dict(
                        {
                            "author": datasets.Value("string"),
                            "work_time": datasets.Value("int32"),
                            "quality_score": datasets.Value("int32"),
                            "creativity_words": datasets.Sequence(datasets.Value("string")),
                        }
                    ),
                    "answer_options": dict(
                        {
                            "A": datasets.Value("string"),
                            "B": datasets.Value("string"),
                            "C": datasets.Value("string"),
                            "D": datasets.Value("string")
                        }
                    ),
                    "answer": datasets.Value("string"),
                    "question_is_multiple": datasets.Value("bool")
                }
            )
        else:
            features = datasets.Features(
                {
                    "idx": dict(
                        {"story": datasets.Value("string"),
                         "question": datasets.Value("int32")
                         }
                    ),
                    "question": datasets.Value("string"),
                    "story": datasets.Value("string"),
                    "properties": dict(
                        {
                            "author": datasets.Value("string"),
                            "work_time": datasets.Value("int32"),
                        }
                    ),
                    "answer_options": dict(
                        {
                            "A": datasets.Value("string"),
                            "B": datasets.Value("string"),
                            "C": datasets.Value("string"),
                            "D": datasets.Value("string")
                        }
                    ),
                    "answer": datasets.Value("string"),
                    "question_is_multiple": datasets.Value("bool")
                }
            )
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            homepage=_HOMEPAGE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        data_dir = os.path.join(dl_manager.download_and_extract(_DATA_URL), 'mctest')
        paths = {}
        for phase in ["train", "dev", "test"]:
            paths[phase] = {
                "data": os.path.join(data_dir, "MCTest", f"{self.config.name}.{phase}.tsv"),
                "answer": os.path.join(data_dir, "MCTest", f"{self.config.name}.{phase}.ans")
            }
        paths["test"]["answer"] = os.path.join(data_dir, "MCTestAnswers", f"{self.config.name}.test.ans")

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={"filepath": paths["train"]},
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={"filepath": paths["dev"]},
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={"filepath": paths["test"]},
            ),
        ]

    def _get_properties(self, property_str):
        """
         properties is a semicolon-delimited list of property:value pairs, including
         Author (anonymized author id, consistent across all files)
         Work Time(s): Seconds between author accepting and submitting the task
         Qual. score: The author's grammar qualification test score (% correct)
         Creativity Words: Words the author was given to encourage creativity
         (there are no creativity words or qual score for mc160, see paper)
        :param property_str:
        :return:
        """
        properties = property_str.split(';')
        property_data = {
            "author": properties[0].split(':')[-1].strip(),
            "work_time": int(properties[1].split(':')[-1].strip())
        }
        if self.config.name == "mc500":
            property_data.update(
                {
                    "quality_score": int(properties[2].split(':')[-1].strip()),
                    "creativity_words": properties[3].split(':')[-1].strip().split(',')
                }
            )
        return property_data

    def _generate_examples(self, filepath):
        tab_char = '\t'
        data_path = filepath['data']
        ans_path = filepath['answer']
        data_lines = open(data_path, encoding="utf-8").read().split('\n')[:-1]
        answer_lines = open(ans_path, encoding="utf-8").read().split('\n')[:-1]
        for data_line, answer_line in zip(data_lines, answer_lines):
            data_line_split = data_line.split(tab_char)
            story_id = data_line_split[0]
            properties = self._get_properties(data_line_split[1])
            story = data_line_split[2]
            answers = answer_line.split('\t')
            data_line_split = data_line_split[3:]
            for i in range(4):
                answer = answers[i]
                index = i*5
                multiple, question_text = [x.strip() for x in data_line_split[index].strip().split(':')]
                question_is_multiple = True if multiple == "multiple" else False
                answer_options = {x: y for x, y in zip(["A", "B", "C", "D"], data_line_split[index+1:index+5])}
                yield f"{story_id}-{i}", {
                    "idx":
                        {"story": story_id,
                         "question": i
                         },
                    "question": question_text,
                    "story": story,
                    "properties": properties,
                    "answer_options": answer_options,
                    "answer": answer,
                    "question_is_multiple": question_is_multiple
                }