propsegment.py

# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# 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.

"""PropSegmEnt: A Large-Scale Corpus for Proposition-Level Segmentation and Entailment Recognition."""


import csv
import json
import os

import datasets

_CITATION = """\
@inproceedings{chen2023propsegment,
    title = "{PropSegmEnt}: A Large-Scale Corpus for Proposition-Level Segmentation and Entailment Recognition",
    author = "Chen, Sihao and Buthpitiya, Senaka and Fabrikant, Alex and Roth, Dan  and Schuster, Tal",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
    year = "2023",
}
"""

# TODO: Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """\
This is a reproduced (i.e. after web-crawling) and processed version of the "PropSegment" dataset from Google Research.

Since the News portion of the dataset is released only via urls, we reconstruct the dataset by crawling. Overall, ~96% 
of the dataset can be reproduced, and the rest ~4% either have url no longer valid, or sentences that have been edited 
(i.e. cannot be aligned with the orignial dataset).

PropSegment (Proposition-level Segmentation and Entailment) is a large-scale, human annotated dataset for segmenting 
English text into propositions, and recognizing proposition-level entailment relations --- whether a different, related 
document entails each proposition, contradicts it, or neither.

The original dataset features >45k human annotated propositions, i.e. individual semantic units within sentences, as 
well as >45k entailment labels between propositions and documents.
"""

_HOMEPAGE = "https://github.com/google-research-datasets/PropSegmEnt"

_LICENSE = "CC-BY-4.0"

# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
_URL = "https://raw.githubusercontent.com/schen149/PropSegmEnt/main/"
_URLS = {
    "segmentation": {
        "train": _URL + "proposition_segmentation.train.jsonl",
        "dev": _URL + "proposition_segmentation.dev.jsonl",
        "test": _URL + "proposition_segmentation.test.jsonl",
    },
    "nli": {
        "train": _URL + "propnli.train.jsonl",
        "dev": _URL + "propnli.dev.jsonl",
        "test": _URL + "propnli.test.jsonl",
    }
}

_CONFIG_TO_FILENAME = {
    "segmentation": "proposition_segmentation",
    "nli": "propnli"
}

class PropSegment(datasets.GeneratorBasedBuilder):

    VERSION = datasets.Version("1.0.0")

    # This is an example of a dataset with multiple configurations.
    # If you don't want/need to define several sub-sets in your dataset,
    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.

    # If you need to make complex sub-parts in the datasets with configurable options
    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
    # BUILDER_CONFIG_CLASS = MyBuilderConfig

    # You will be able to load one or the other configurations in the following list with
    # data = datasets.load_dataset('my_dataset', 'first_domain')
    # data = datasets.load_dataset('my_dataset', 'second_domain')
    BUILDER_CONFIGS = [
        datasets.BuilderConfig(name="segmentation", version=VERSION, description="This part of my dataset covers a first domain"),
        datasets.BuilderConfig(name="nli", version=VERSION, description="This part of my dataset covers a second domain"),
    ]

    DEFAULT_CONFIG_NAME = "segmentation"  # It's not mandatory to have a default configuration. Just use one if it make sense.

    def _info(self):
        if self.config.name == "segmentation":  # This is the name of the configuration selected in BUILDER_CONFIGS above
            features = datasets.Features(
                {
                    "sentence": datasets.Value("string"),
                    "propositions": datasets.Value("string"),
                }
            )
        else:
            features = datasets.Features(
                {
                    "hypothesis": datasets.Value("string"),
                    "premise": datasets.Value("string"),
                    "label": datasets.Value("string")
                }
            )
        return datasets.DatasetInfo(
            # This is the description that will appear on the datasets page.
            description=_DESCRIPTION,
            # This defines the different columns of the dataset and their types
            features=features,  # Here we define them above because they are different between the two configurations
            # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
            # supervised_keys=("sentence", "label"),
            # Homepage of the dataset for documentation
            homepage=_HOMEPAGE,
            # License for the dataset if available
            license=_LICENSE,
            # Citation for the dataset
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        config_name = self.config.name
        urls = _URLS[config_name]
        data_dir = dl_manager.download_and_extract(urls)
        file_prefix = _CONFIG_TO_FILENAME[config_name]

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": os.path.join(
                        data_dir, "{}.train.jsonl".format(file_prefix)),
                    "split": "train",
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": os.path.join(
                        data_dir, "{}.dev.jsonl".format(file_prefix)),
                    "split": "dev",
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": os.path.join(
                        data_dir, "{}.test.jsonl".format(file_prefix)),
                    "split": "test"
                },
            ),
        ]

    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
    def _generate_examples(self, filepath, split):
        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
        with open(filepath, encoding="utf-8") as f:
            for key, row in enumerate(f):
                data = json.loads(row)
                if self.config.name == "segmentation":
                    yield key, {
                        "sentence": data["sentence"],
                        "propositions": data["propositions"],
                    }
                else:
                    yield key, {
                        "hypothesis": data["hypothesis"],
                        "premise": data["premise"],
                        "label": data["label"],
                    }