LVSTCK/nq_open-mk

Tasks

• question-answering

Citation text from the card

@article{doi:10.1162/tacl\_a\_00276,
    author = {Kwiatkowski, Tom and Palomaki, Jennimaria and Redfield, Olivia and Collins, Michael and Parikh, Ankur and Alberti, Chris and Epstein, Danielle and Polosukhin, Illia and Devlin, Jacob and Lee, Kenton and Toutanova, Kristina and Jones, Llion and Kelcey, Matthew and Chang, Ming-Wei and Dai, Andrew                         M. and Uszkoreit, Jakob and Le, Quoc and Petrov, Slav},
    title = {Natural Questions: A Benchmark for Question Answering Research},
    journal = {Transactions of the Association for Computational Linguistics},
    volume = {7},
    number = {},
    pages = {453-466},
    year = {2019},
    doi = {10.1162/tacl\_a\_00276},
    URL = { 
            https://doi.org/10.1162/tacl_a_00276
        },
    eprint = { 
            https://doi.org/10.1162/tacl_a_00276
        
        },
    abstract = { We present the Natural Questions corpus, a question answering data set. Questions consist of real anonymized, aggregated queries issued to the Google search engine. An annotator is presented with a question along with a Wikipedia page from the top 5 search results, and annotates a long answer (typically a paragraph) and a short answer (one or more entities) if present on the page, or marks null if no long/short answer is present. The public release consists of 307,373 training examples with single annotations; 7,830 examples with 5-way annotations for development data; and a further 7,842 examples with 5-way annotated se

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