May 26, 2022, 1:12 a.m. | Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Dmytro Okhonko, Samuel Broscheit, Gautier Izacard, Patrick Lewis, Barlas Oğuz, Edouard Gra

cs.CL updates on arXiv.org arxiv.org

In order to address increasing demands of real-world applications, the
research for knowledge-intensive NLP (KI-NLP) should advance by capturing the
challenges of a truly open-domain environment: web-scale knowledge, lack of
structure, inconsistent quality and noise. To this end, we propose a new setup
for evaluating existing knowledge intensive tasks in which we generalize the
background corpus to a universal web snapshot. We investigate a slate of NLP
tasks which rely on knowledge - either factual or common sense, and ask …

arxiv knowledge nlp web

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