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inquisitiveqg.py

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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/d0bc26276851fdcd375ec5c91fb2a593a2171a8e/article.zip"
+_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