Datasets:

bigbio
/

medhop


	---
	language:
	- en
	bigbio_language:
	- English
	license: cc-by-sa-3.0
	multilinguality: monolingual
	bigbio_license_shortname: CC_BY_SA_3p0
	pretty_name: MedHop
	homepage: http://qangaroo.cs.ucl.ac.uk/
	bigbio_pubmed: True
	bigbio_public: True
	bigbio_tasks:
	- QUESTION_ANSWERING
	---


	# Dataset Card for MedHop

	## Dataset Description

	- Homepage: http://qangaroo.cs.ucl.ac.uk/
	- Pubmed: True
	- Public: True
	- Tasks: QA


	With the same format as WikiHop, this dataset is based on research paper
	abstracts from PubMed, and the queries are about interactions between
	pairs of drugs. The correct answer has to be inferred by combining
	information from a chain of reactions of drugs and proteins.



	## Citation Information

	```
	@article{welbl-etal-2018-constructing,
	title = Constructing Datasets for Multi-hop Reading Comprehension Across Documents,
	author = Welbl, Johannes and Stenetorp, Pontus and Riedel, Sebastian,
	journal = Transactions of the Association for Computational Linguistics,
	volume = 6,
	year = 2018,
	address = Cambridge, MA,
	publisher = MIT Press,
	url = https://aclanthology.org/Q18-1021,
	doi = 10.1162/tacl_a_00021,
	pages = 287--302,
	abstract = {
	Most Reading Comprehension methods limit themselves to queries which
	can be answered using a single sentence, paragraph, or document.
	Enabling models to combine disjoint pieces of textual evidence would
	extend the scope of machine comprehension methods, but currently no
	resources exist to train and test this capability. We propose a novel
	task to encourage the development of models for text understanding
	across multiple documents and to investigate the limits of existing
	methods. In our task, a model learns to seek and combine evidence
	-- effectively performing multihop, alias multi-step, inference.
	We devise a methodology to produce datasets for this task, given a
	collection of query-answer pairs and thematically linked documents.
	Two datasets from different domains are induced, and we identify
	potential pitfalls and devise circumvention strategies. We evaluate
	two previously proposed competitive models and find that one can
	integrate information across documents. However, both models
	struggle to select relevant information; and providing documents
	guaranteed to be relevant greatly improves their performance. While
	the models outperform several strong baselines, their best accuracy
	reaches 54.5 % on an annotated test set, compared to human
	performance at 85.0 %, leaving ample room for improvement.
	}

	```