chore: mctest

README.md

@@ -1 +1,144 @@
-hello
+---
+annotations_creators:
+- expert-generated
+language_creators:
+- found
+languages:
+- en-US
+licenses:
+- microsoft-research-license
+multilinguality:
+- monolingual
+size_categories:
+- 1K<n<10K
+source_datasets:
+- extended|natural_questions
+task_categories:
+- question-answering
+task_ids:
+- multiple-choice-qa
+- question-answering-other-explanations-in-question-answering
+paperswithcode_id: mctest
+---
+
+# Dataset Card Creation Guide
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage:** N/A
+- **Repository:** [GitHub](https://github.com/mcobzarenco/mctest/)
+- **Paper:** [MCTest: A Challenge Dataset for the Open-Domain Machine Comprehension of Text](https://www.aclweb.org/anthology/D13-1020.pdf)
+- **Leaderboard:** N/A
+- **Point of Contact:** -
+
+### Dataset Summary
+
+[More Information Needed]
+
+### Supported Tasks and Leaderboards
+
+[More Information Needed]
+
+### Languages
+
+[More Information Needed]
+
+## Dataset Structure
+
+### Data Instances
+
+[More Information Needed]
+
+### Data Fields
+
+[More Information Needed]
+
+### Data Splits
+
+[More Information Needed]
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+[More Information Needed]
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed]
+
+#### Who are the source language producers?
+
+[More Information Needed]
+
+### Annotations
+
+[More Information Needed]
+
+#### 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
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed]
+
+### Licensing Information
+
+[More Information Needed]
+
+### Citation Information
+
+[More Information Needed]
+### Contributions

dataset_infos.json

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+{"mc500": {"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.\n", "citation": "@inproceedings{richardson-etal-2013-mctest,\n    title = \"{MCT}est: A Challenge Dataset for the Open-Domain Machine Comprehension of Text\",\n    author = \"Richardson, Matthew  and\n      Burges, Christopher J.C.  and\n      Renshaw, Erin\",\n    booktitle = \"Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing\",\n    month = oct,\n    year = \"2013\",\n    address = \"Seattle, Washington, USA\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/D13-1020\",\n    pages = \"193--203\",\n}\n", "homepage": "https://www.aclweb.org/anthology/D13-1020/", "license": "", "features": {"idx": {"story": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "int32", "id": null, "_type": "Value"}}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "story": {"dtype": "string", "id": null, "_type": "Value"}, "properties": {"author": {"dtype": "string", "id": null, "_type": "Value"}, "work_time": {"dtype": "int32", "id": null, "_type": "Value"}, "quality_score": {"dtype": "int32", "id": null, "_type": "Value"}, "creativity_words": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "answer_options": {"A": {"dtype": "string", "id": null, "_type": "Value"}, "B": {"dtype": "string", "id": null, "_type": "Value"}, "C": {"dtype": "string", "id": null, "_type": "Value"}, "D": {"dtype": "string", "id": null, "_type": "Value"}}, "answer": {"dtype": "string", "id": null, "_type": "Value"}, "question_is_multiple": {"dtype": "bool", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "mc_test", "config_name": "mc500", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 1743546, "num_examples": 1200, "dataset_name": "mc_test"}, "validation": {"name": "validation", "num_bytes": 286746, "num_examples": 200, "dataset_name": "mc_test"}, "test": {"name": "test", "num_bytes": 840244, "num_examples": 600, "dataset_name": "mc_test"}}, "download_checksums": {"http://parl.ai/downloads/mctest/mctest.tar.gz": {"num_bytes": 1181387, "checksum": "c8160bf790c97cec8e272677600170d5e181649492bde7e2c0ea5fb23ab25af7"}}, "download_size": 1181387, "post_processing_size": null, "dataset_size": 2870536, "size_in_bytes": 4051923}, "mc160": {"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.\n", "citation": "@inproceedings{richardson-etal-2013-mctest,\n    title = \"{MCT}est: A Challenge Dataset for the Open-Domain Machine Comprehension of Text\",\n    author = \"Richardson, Matthew  and\n      Burges, Christopher J.C.  and\n      Renshaw, Erin\",\n    booktitle = \"Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing\",\n    month = oct,\n    year = \"2013\",\n    address = \"Seattle, Washington, USA\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://www.aclweb.org/anthology/D13-1020\",\n    pages = \"193--203\",\n}\n", "homepage": "https://www.aclweb.org/anthology/D13-1020/", "license": "", "features": {"idx": {"story": {"dtype": "string", "id": null, "_type": "Value"}, "question": {"dtype": "int32", "id": null, "_type": "Value"}}, "question": {"dtype": "string", "id": null, "_type": "Value"}, "story": {"dtype": "string", "id": null, "_type": "Value"}, "properties": {"author": {"dtype": "string", "id": null, "_type": "Value"}, "work_time": {"dtype": "int32", "id": null, "_type": "Value"}}, "answer_options": {"A": {"dtype": "string", "id": null, "_type": "Value"}, "B": {"dtype": "string", "id": null, "_type": "Value"}, "C": {"dtype": "string", "id": null, "_type": "Value"}, "D": {"dtype": "string", "id": null, "_type": "Value"}}, "answer": {"dtype": "string", "id": null, "_type": "Value"}, "question_is_multiple": {"dtype": "bool", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "mc_test", "config_name": "mc160", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 371573, "num_examples": 280, "dataset_name": "mc_test"}, "validation": {"name": "validation", "num_bytes": 159916, "num_examples": 120, "dataset_name": "mc_test"}, "test": {"name": "test", "num_bytes": 302109, "num_examples": 240, "dataset_name": "mc_test"}}, "download_checksums": {"http://parl.ai/downloads/mctest/mctest.tar.gz": {"num_bytes": 1181387, "checksum": "c8160bf790c97cec8e272677600170d5e181649492bde7e2c0ea5fb23ab25af7"}}, "download_size": 1181387, "post_processing_size": null, "dataset_size": 833598, "size_in_bytes": 2014985}}

mctest.py

@@ -0,0 +1,214 @@
+# 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.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""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
+                }
+