propsegment-retrieval.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.

"""Atomic Fact Retrieval Task of PropSegmEnt."""


import csv
import json
import os

import datasets

_CITATION = """\
@article{chen2023subsentence,
  title={Sub-Sentence Encoder: Contrastive Learning of Propositional Semantic Representations},
  author={Sihao Chen and Hongming Zhang and Tong Chen and Ben Zhou and Wenhao Yu and Dian Yu and Baolin Peng and Hongwei Wang and Dan Roth and Dong Yu},
  journal={arXiv preprint arXiv:2311.04335},
  year={2023},
  URL = {https://arxiv.org/pdf/2311.04335.pdf}
}

@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 contains the processed dataset for the atomic fact retrieval task of the "PropSegment" dataset.

The task features a test set of 8,865 queries propositions. 
Each query proposition corresponds to 1-2 ground truth propositions from another document. 
In total, there are 43,299 target candidate propositions. 
Note that the query propositions are also included in the target set, so during evaluation, the query needs to be removed from the retrieved candidates.    

Check out more details in our paper -- https://arxiv.org/pdf/2311.04335.pdf. 
"""

_HOMEPAGE = "https://github.com/schen149/sub-sentence-encoder"

_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)
_URLS = {
    "targets": {
        "test": "propsegment_targets_all.jsonl",
    },
    "queries": {
        "test": "propsegment_queries_all.jsonl",
    }
}

_CONFIG_TO_FILENAME = {
    "targets": "propsegment_targets_all",
    "queries": "propsegment_queries_all"
}

class PropSegmentRetrieval(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="targets", version=VERSION, description="Query propositions of the atomic fact retrieval task"),
        datasets.BuilderConfig(name="queries", version=VERSION, description="Target candidate propositions of the atomic fact retrieval task"),
    ]

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

    def _info(self):
        if self.config.name == "queries":  # This is the name of the configuration selected in BUILDER_CONFIGS above
            features = datasets.Features(
                {
                    "id": datasets.Value("string"),
                    "sentence_text": datasets.Value("string"),
                    "spans": datasets.Value("string"),
                    "label": datasets.features.Sequence(datasets.Value("string")),
                    "tokens": datasets.features.Sequence(
                        {"text": datasets.Value("string"), "character_offset_of_token_in_sentence": datasets.Value("int32"),}
                    ),
                    "token_indices": datasets.features.Sequence(datasets.Value("int32"))
                }
            )
        else:
            features = datasets.Features(
                {
                    "id": datasets.Value("string"),
                    "sentence_text": datasets.Value("string"),
                    "spans": datasets.Value("string"),
                    "tokens": datasets.features.Sequence(
                        {"text": datasets.Value("string"), "character_offset_of_token_in_sentence": datasets.Value("int32"),}
                    ),
                    "token_indices": datasets.features.Sequence(datasets.Value("int32"))
                }
            )
        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(urls)
        file_prefix = _CONFIG_TO_FILENAME[config_name]

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": data_dir["test"],
                    "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 == "queries":
                    yield key, {
                        "id": data["id"],
                        "sentence_text": data["sentence_text"],
                        "spans": data["spans"],
                        "label": data["label"],
                        "tokens": data["tokens"],
                        "token_indices": data["token_indices"],
                    }
                else:
                    yield key, {
                        "id": data["id"],
                        "sentence_text": data["sentence_text"],
                        "spans": data["spans"],
                        "tokens": data["tokens"],
                        "token_indices": data["token_indices"],
                    }