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We study the role of linguistic context in predicting quantifiers (few', all’). We collect crowdsourced data from human participants and test various
models in a local (single-sentence) and a global context (multi-sentence)
condition. Models significantly out-perform humans in the former setting and
are only slightly better in the latter. While human performance improves with
more linguistic context (especially on proportional quantifiers), model
performance suffers. Models are very effective in exploiting lexical and
morpho-syntactic patterns; humans are better at genuinely understanding the
meaning of the (global) context.

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