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CALaMo: a Constructionist Assessment of Language Models
This paper presents a novel framework for evaluating Neural Language Models' linguistic abilities using a constructionist approach.
Novel Aficionados and Doppelgängers: a referential task for semantic representations of individual entities
In human semantic cognition, proper names (names which refer to individual entities) are harder to learn and retrieve than common nouns.
Recurrent babbling: evaluating the acquisition of grammar from limited input data
Recurrent Neural Networks (RNNs) have been shown to capture various aspects of syntax from raw linguistic input.
Pay Attention to Those Sets! Learning Quantification from Images
We however argue that precisely identifying the composition of the sets is not only beyond current state-of-the-art models but perhaps even detrimental to a task that is most efficiently performed by refining the approximate numerosity estimator of the system.
