upload hub_repos/medhop/README.md to hub from bigbio repo

README.md

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+
+---
+language: 
+- en
+license: cc-by-sa-3.0
+license_bigbio_shortname: CC_BY_SA_3p0
+pretty_name: MedHop
+---
+
+
+# Dataset Card for MedHop
+
+## Dataset Description
+
+- **Homepage:** http://qangaroo.cs.ucl.ac.uk/
+- **Pubmed:** True
+- **Public:** True
+- **Tasks:** Question Answering
+
+
+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.
+}
+
+```