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import json |
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import os |
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import datasets |
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from .bigbiohub import qa_features |
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from .bigbiohub import BigBioConfig |
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from .bigbiohub import Tasks |
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_LANGUAGES = ['English'] |
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_PUBMED = True |
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_LOCAL = False |
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_CITATION = """\ |
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@article{welbl-etal-2018-constructing, |
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title = Constructing Datasets for Multi-hop Reading Comprehension Across Documents, |
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author = Welbl, Johannes and Stenetorp, Pontus and Riedel, Sebastian, |
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journal = Transactions of the Association for Computational Linguistics, |
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volume = 6, |
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year = 2018, |
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address = Cambridge, MA, |
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publisher = MIT Press, |
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url = https://aclanthology.org/Q18-1021, |
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doi = 10.1162/tacl_a_00021, |
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pages = 287--302, |
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abstract = { |
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Most Reading Comprehension methods limit themselves to queries which |
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can be answered using a single sentence, paragraph, or document. |
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Enabling models to combine disjoint pieces of textual evidence would |
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extend the scope of machine comprehension methods, but currently no |
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resources exist to train and test this capability. We propose a novel |
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task to encourage the development of models for text understanding |
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across multiple documents and to investigate the limits of existing |
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methods. In our task, a model learns to seek and combine evidence |
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-- effectively performing multihop, alias multi-step, inference. |
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We devise a methodology to produce datasets for this task, given a |
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collection of query-answer pairs and thematically linked documents. |
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Two datasets from different domains are induced, and we identify |
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potential pitfalls and devise circumvention strategies. We evaluate |
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two previously proposed competitive models and find that one can |
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integrate information across documents. However, both models |
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struggle to select relevant information; and providing documents |
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guaranteed to be relevant greatly improves their performance. While |
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the models outperform several strong baselines, their best accuracy |
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reaches 54.5 % on an annotated test set, compared to human |
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performance at 85.0 %, leaving ample room for improvement. |
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} |
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""" |
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_DESCRIPTION = """\ |
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With the same format as WikiHop, this dataset is based on research paper |
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abstracts from PubMed, and the queries are about interactions between |
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pairs of drugs. The correct answer has to be inferred by combining |
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information from a chain of reactions of drugs and proteins. |
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""" |
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_DATASETNAME = "medhop" |
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_DISPLAYNAME = "MedHop" |
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_HOMEPAGE = "http://qangaroo.cs.ucl.ac.uk/" |
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_LICENSE = 'Creative Commons Attribution Share Alike 3.0 Unported' |
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_BASE_GDRIVE = "https://drive.google.com/uc?export=download&confirm=yTib&id=" |
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_URLs = { |
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"source": _BASE_GDRIVE + "1ytVZ4AhubFDOEL7o7XrIRIyhU8g9wvKA", |
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"bigbio_qa": _BASE_GDRIVE + "1ytVZ4AhubFDOEL7o7XrIRIyhU8g9wvKA", |
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} |
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_SUPPORTED_TASKS = [Tasks.QUESTION_ANSWERING] |
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_SOURCE_VERSION = "1.0.0" |
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_BIGBIO_VERSION = "1.0.0" |
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class MedHopDataset(datasets.GeneratorBasedBuilder): |
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"""MedHop""" |
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DEFAULT_CONFIG_NAME = "medhop_source" |
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SOURCE_VERSION = datasets.Version(_SOURCE_VERSION) |
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BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION) |
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BUILDER_CONFIGS = [ |
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BigBioConfig( |
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name="medhop_source", |
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version=SOURCE_VERSION, |
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description="MedHop source schema", |
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schema="source", |
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subset_id="MedHop", |
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), |
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BigBioConfig( |
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name="medhop_bigbio_qa", |
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version=BIGBIO_VERSION, |
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description="MedHop BigBio schema", |
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schema="bigbio_qa", |
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subset_id="MedHop", |
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), |
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] |
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def _info(self): |
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if self.config.schema == "source": |
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features = datasets.Features( |
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{ |
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"id": datasets.Value("string"), |
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"candidates": datasets.Sequence(datasets.Value("string")), |
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"answer": datasets.Value("string"), |
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"supports": datasets.Sequence(datasets.Value("string")), |
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"query": datasets.Value("string"), |
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} |
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) |
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elif self.config.schema == "bigbio_qa": |
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features = qa_features |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=features, |
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supervised_keys=None, |
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homepage=_HOMEPAGE, |
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license=str(_LICENSE), |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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"""Returns SplitGenerators.""" |
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my_urls = _URLs[self.config.schema] |
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data_dir = dl_manager.download_and_extract(my_urls) |
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data_dir += "/qangaroo_v1.1/medhop/" |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={ |
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"filepath": os.path.join(data_dir, "train.json"), |
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"split": "train", |
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}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.VALIDATION, |
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gen_kwargs={ |
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"filepath": os.path.join(data_dir, "dev.json"), |
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"split": "validation", |
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}, |
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), |
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] |
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def _generate_examples(self, filepath, split): |
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"""Yields examples as (key, example) tuples.""" |
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if self.config.schema == "source": |
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with open(filepath, encoding="utf-8") as file: |
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uid = 0 |
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data = json.load(file) |
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for i, record in enumerate(data): |
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yield i, { |
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"id": record["id"], |
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"candidates": record["candidates"], |
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"answer": record["answer"], |
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"supports": record["supports"], |
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"query": record["query"], |
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} |
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uid += 1 |
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elif self.config.schema == "bigbio_qa": |
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with open(filepath, encoding="utf-8") as file: |
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uid = 0 |
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data = json.load(file) |
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for record in data: |
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record["type"] = "multiple_choice" |
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yield uid, { |
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"id": record["id"], |
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"document_id": record["id"], |
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"question_id": record["id"], |
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"question": record["query"], |
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"type": record["type"], |
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"context": " ".join(record["supports"]), |
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"answer": [record["answer"]], |
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"choices": record["candidates"], |
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} |
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uid += 1 |
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