mediqa_rqe.py

# coding=utf-8
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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import glob
import json
import os
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Iterator, Tuple
from xml.etree import ElementTree as ET

import datasets

from .bigbiohub import pairs_features
from .bigbiohub import BigBioConfig
from .bigbiohub import Tasks

_LANGUAGES = ['English']
_PUBMED = False
_LOCAL = False
_CITATION = """\
@inproceedings{MEDIQA2019,
  author    = {Asma {Ben Abacha} and Chaitanya Shivade and Dina Demner{-}Fushman},
  title     = {Overview of the MEDIQA 2019 Shared Task on Textual Inference, Question Entailment and Question Answering},
  booktitle = {ACL-BioNLP 2019},
  year      = {2019}
}
"""

_DATASETNAME = "mediqa_rqe"
_DISPLAYNAME = "MEDIQA RQE"

_DESCRIPTION = """\
The MEDIQA challenge is an ACL-BioNLP 2019 shared task aiming to attract further research efforts in Natural Language Inference (NLI), Recognizing Question Entailment (RQE), and their applications in medical Question Answering (QA).
Mailing List: https://groups.google.com/forum/#!forum/bionlp-mediqa

The objective of the RQE task is to identify entailment between two questions in the context of QA. We use the following definition of question entailment: “a question A entails a question B if every answer to B is also a complete or partial answer to A” [1]
    [1] A. Ben Abacha & D. Demner-Fushman. “Recognizing Question Entailment for Medical Question Answering”. AMIA 2016.
"""

_HOMEPAGE = "https://sites.google.com/view/mediqa2019"
_LICENSE = 'License information unavailable'
_URLS = {
    _DATASETNAME: "https://github.com/abachaa/MEDIQA2019/archive/refs/heads/master.zip"
}

_SUPPORTED_TASKS = [Tasks.TEXT_PAIRS_CLASSIFICATION]
_SOURCE_VERSION = "1.0.0"
_BIGBIO_VERSION = "1.0.0"


class MediqaRQEDataset(datasets.GeneratorBasedBuilder):
    """MediqaRQE Dataset"""

    SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
    BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)

    BUILDER_CONFIGS = [
        # Source Schema
        BigBioConfig(
            name="mediqa_rqe_source",
            version=SOURCE_VERSION,
            description="MEDIQA RQE source schema",
            schema="source",
            subset_id="mediqa_rqe_source",
        ),
        # BigBio Schema
        BigBioConfig(
            name="mediqa_rqe_bigbio_pairs",
            version=BIGBIO_VERSION,
            description="MEDIQA RQE BigBio schema",
            schema="bigbio_pairs",
            subset_id="mediqa_rqe_bigbio_pairs",
        ),
    ]

    DEFAULT_CONFIG_NAME = "mediqa_rqe_source"

    def _info(self):
        if self.config.schema == "source":
            features = datasets.Features(
                {
                    "pid": datasets.Value("string"),
                    "value": datasets.Value("string"),
                    "chq": datasets.Value("string"),
                    "faq": datasets.Value("string"),
                }
            )
        elif self.config.schema == "bigbio_pairs":
            features = pairs_features

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

    def _split_generators(self, dl_manager):
        data_dir = Path(dl_manager.download_and_extract(_URLS[_DATASETNAME]))

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "filepath": data_dir
                    / "MEDIQA2019-master/MEDIQA_Task2_RQE/MEDIQA2019-Task2-RQE-TrainingSet-AMIA2016.xml"
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={
                    "filepath": data_dir
                    / "MEDIQA2019-master/MEDIQA_Task2_RQE/MEDIQA2019-Task2-RQE-ValidationSet-AMIA2016.xml"
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "filepath": data_dir
                    / "MEDIQA2019-master/MEDIQA_Task2_RQE/MEDIQA2019-Task2-RQE-TestSet-wLabels.xml"
                },
            ),
        ]

    def _generate_examples(self, filepath: Path) -> Iterator[Tuple[str, Dict]]:
        dom = ET.parse(filepath).getroot()
        for row in dom.iterfind("pair"):
            pid = row.attrib["pid"]
            value = row.attrib["value"]
            chq = row.find("chq").text.strip()
            faq = row.find("faq").text.strip()

            if self.config.schema == "source":
                yield pid, {"pid": pid, "value": value, "chq": chq, "faq": faq}
            elif self.config.schema == "bigbio_pairs":
                yield pid, {
                    "id": pid,
                    "document_id": pid,
                    "text_1": chq,
                    "text_2": faq,
                    "label": value,
                }