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--- |
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language: |
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- en |
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bigbio_language: |
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- English |
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license: cc-by-sa-3.0 |
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multilinguality: monolingual |
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bigbio_license_shortname: CC_BY_SA_3p0 |
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pretty_name: MedHop |
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homepage: http://qangaroo.cs.ucl.ac.uk/ |
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bigbio_pubmed: True |
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bigbio_public: True |
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bigbio_tasks: |
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- QUESTION_ANSWERING |
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--- |
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# Dataset Card for MedHop |
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## Dataset Description |
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- **Homepage:** http://qangaroo.cs.ucl.ac.uk/ |
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- **Pubmed:** True |
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- **Public:** True |
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- **Tasks:** QA |
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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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## Citation Information |
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``` |
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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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