bigbio/medhop

Citation text from the card

@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

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