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This work aims at modeling how the meaning of gradable adjectives of size(`big', `small') can be learned from visually-grounded contexts. Inspired bycognitive and linguistic evidence showing that the use of these expressionsrelies on setting a threshold that is dependent on a specific context, weinvestigate the ability of multi-modal models in assessing whether an object is`big' or `small' in a given visual scene. In contrast with the standardcomputational approach that simplistically treats gradable adjectives as`fixed' attributes, we pose the problem as relational: to be successful, amodel has to consider the full visual context. By means of four main tasks, weshow that state-of-the-art models (but not a relatively strong baseline) canlearn the function subtending the meaning of size adjectives, though theirperformance is found to decrease while moving from simple to more complextasks. Crucially, models fail in developing abstract representations ofgradable adjectives that can be used compositionally.

Is the Red Square Big? MALeViC: Modeling Adjectives Leveraging Visual Contexts