QTUNA: A Corpus for Understanding How Speakers Use Quantification

WS 2019 · Guanyi Chen, Kees Van Deemter, Silvia Pagliaro, Louk Smalbil, Chenghua Lin ·

A prominent strand of work in formal semantics investigates the ways in which human languages quantify over the elements of a set, as when we say {``}\textit{All $A$ are $B$ }{''}, {``}\textit{All except two $A$ are $B$ }{''}, {``}\textit{Only a few of the $A$ are $B$ }{''} and so on. Our aim is to build Natural Language Generation algorithms that mimic humans{'} use of quantified expressions. To inform these algorithms, we conducted on a series of elicitation experiments in which human speakers were asked to perform a linguistic task that invites the use of quantified expressions. We discuss how these experiments were conducted and what corpora they gave rise to. We conduct an informal analysis of the corpora, and offer an initial assessment of the challenges that these corpora pose for Natural Language Generation. The dataset is available at:{\textasciitilde}\url{https://github.com/a-quei/qtuna}.

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