Datasets:
Tasks:
Text2Text Generation
Languages:
English
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Language Creators:
crowdsourced
Annotations Creators:
crowdsourced
Source Datasets:
original
Tags:
question-generation
License:
File size: 6,697 Bytes
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# coding=utf-8
# Copyright 2020 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.
# Lint as: python3
"""Inquisitive Question Generation for High Level Text Comprehension"""
import itertools
import datasets
_CITATION = """\
@InProceedings{ko2020inquisitive,
author = {Ko, Wei-Jen and Chen, Te-Yuan and Huang, Yiyan and Durrett, Greg and Li, Junyi Jessy},
title = {Inquisitive Question Generation for High Level Text Comprehension},
booktitle = {Proceedings of EMNLP},
year = {2020},
}
"""
_DESCRIPTION = """\
A dataset of about 20k questions that are elicited from readers as they naturally read through a document sentence by sentence. \
Compared to existing datasets, INQUISITIVE questions target more towards high-level (semantic and discourse) comprehension of text. \
Because these questions are generated while the readers are processing the information, the questions directly communicate gaps between \
the reader’s and writer’s knowledge about the events described in the text, and are not necessarily answered in the document itself. \
This type of question reflects a real-world scenario: if one has questions during reading, some of them are answered by the text later on, \
the rest are not, but any of them would help further the reader’s understanding at the particular point when they asked it. \
This resource could enable question generation models to simulate human-like curiosity and cognitive processing, which may open up a new realm of applications.
"""
_ARTICLES_URL = "https://github.com/wjko2/INQUISITIVE/raw/master/articles.tgz"
_QUESTIONS_URL = "https://github.com/wjko2/INQUISITIVE/raw/master/questions.txt"
ALL_ARTICLE_IDS = list(range(1, 1501))
DEV_ARTICLE_IDS = list(itertools.chain(range(1, 101), range(1051, 1101)))
TEST_ARTICLE_IDS = list(itertools.chain(range(101, 151), range(501, 551), range(1101, 1151)))
DEV_AND_TEST_IDS = DEV_ARTICLE_IDS + TEST_ARTICLE_IDS
TRAIN_ARTICLE_IDS = [id_ for id_ in ALL_ARTICLE_IDS if id_ not in DEV_AND_TEST_IDS]
class InquisitiveQgConfig(datasets.BuilderConfig):
"""BuilderConfig for INQUISITIVE."""
def __init__(self, **kwrags):
"""BuilderConfig for INQUISITIVE.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(InquisitiveQgConfig, self).__init__(**kwrags)
class InquisitiveQg(datasets.GeneratorBasedBuilder):
"""Inquisitive Question Generation for High Level Text Comprehension"""
VERSION = datasets.Version("1.0.0")
BUILDER_CONFIGS = [
InquisitiveQgConfig(name="plain_text", version=datasets.Version("1.0.0", ""), description="plain_text"),
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"id": datasets.Value("int32"),
"article_id": datasets.Value("int32"),
"article": datasets.Value("string"),
"sentence_id": datasets.Value("int32"),
"sentence": datasets.Value("string"),
"span": datasets.Value("string"),
"question": datasets.Value("string"),
"span_start_position": datasets.Value("int32"),
"span_end_position": datasets.Value("int32"),
}
),
supervised_keys=None,
homepage="https://github.com/wjko2/INQUISITIVE",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
questions_file = dl_manager.download(_QUESTIONS_URL)
archive = dl_manager.download(_ARTICLES_URL)
articles_dir = "article"
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"articles_dir": articles_dir,
"questions_file": questions_file,
"article_ids": TRAIN_ARTICLE_IDS,
"files": dl_manager.iter_archive(archive),
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"articles_dir": articles_dir,
"questions_file": questions_file,
"article_ids": DEV_ARTICLE_IDS,
"files": dl_manager.iter_archive(archive),
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"articles_dir": articles_dir,
"questions_file": questions_file,
"article_ids": TEST_ARTICLE_IDS,
"files": dl_manager.iter_archive(archive),
},
),
]
def _generate_examples(self, articles_dir, questions_file, article_ids, files):
articles = {}
for path, f in files:
articles[path] = f.read().decode("utf-8")
with open(questions_file, encoding="utf-8") as f:
questions_counter = 0
rows = f.readlines()
for i, row in enumerate(rows):
if i == 0:
continue # skip header line
row = row.strip()
cols = row.split("\t")
article_id = int(cols[0])
if article_id not in article_ids:
continue
fname = str(article_id).rjust(4, "0") + ".txt"
article_path = articles_dir + "/" + fname
article = articles[article_path]
id_ = str(questions_counter)
example = {
"article_id": article_id,
"sentence_id": int(cols[1]),
"sentence": cols[2],
"span": cols[3],
"question": cols[4],
"span_start_position": cols[5],
"span_end_position": cols[6],
"id": id_,
"article": article,
}
yield id_, example
questions_counter += 1
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