rumoureval_2019.py

# Copyright 2022 Mads Kongsbak and Leon Derczynski
#
# 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.
# TODO: Address all TODOs and remove all explanatory comments
"""RumourEval 2019: Stance Prediction"""


import csv
import json
import os

import datasets


# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@inproceedings{gorrell-etal-2019-semeval,
    title = "{S}em{E}val-2019 Task 7: {R}umour{E}val, Determining Rumour Veracity and Support for Rumours",
    author = "Gorrell, Genevieve  and
      Kochkina, Elena  and
      Liakata, Maria  and
      Aker, Ahmet  and
      Zubiaga, Arkaitz  and
      Bontcheva, Kalina  and
      Derczynski, Leon",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/S19-2147",
    doi = "10.18653/v1/S19-2147",
    pages = "845--854",
}

"""

# TODO: Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """\

Stance prediction task in English. The goal is to predict whether a given reply to a claim either supports, denies, questions, or simply comments on the claim. Ran as a SemEval task in 2019.
"""

# TODO: Add a link to an official homepage for the dataset here
_HOMEPAGE = ""

# TODO: Add the licence for the dataset here if you can find it
_LICENSE = "cc-by-4.0"

class RumourEval2019Config(datasets.BuilderConfig):

    def __init__(self, **kwargs):
        super(RumourEval2019Config, self).__init__(**kwargs)

class RumourEval2019(datasets.GeneratorBasedBuilder):
    """RumourEval2019 Stance Detection Dataset formatted in triples of (source_text, reply_text, label)"""

    VERSION = datasets.Version("1.0.0")

    BUILDER_CONFIGS = [
        RumourEval2019Config(name="RumourEval2019", version=VERSION, description="Stance Detection Dataset"),
    ]
    
    def _info(self):
        features = datasets.Features(
            {
                "id": datasets.Value("string"),
                "source_text": datasets.Value("string"),
                "reply_text": datasets.Value("string"),
                "label": datasets.features.ClassLabel(
                    names=[
                        "support",
                        "deny",
                        "query",
                        "comment"
                    ]
                )
            }
        )

        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        train_text = dl_manager.download_and_extract("rumoureval2019_train.csv")
        validation_text = dl_manager.download_and_extract("rumoureval2019_val.csv")
        test_text = dl_manager.download_and_extract("rumoureval2019_test.csv")
        
        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN,      gen_kwargs={"filepath": train_text, "split": "train"}),
            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": validation_text, "split": "validation"}),
            datasets.SplitGenerator(name=datasets.Split.TEST,       gen_kwargs={"filepath": test_text, "split": "test"}),
        ]

    def _generate_examples(self, filepath, split):
        with open(filepath, encoding="utf-8") as f:
            reader = csv.DictReader(f, delimiter=",")
            guid = 0
            for instance in reader:
                instance["source_text"] = instance.pop("source_text")
                instance["reply_text"] = instance.pop("reply_text")
                instance["label"] = instance.pop("label")
                instance['id'] = str(guid)
                yield guid, instance
                guid += 1