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

本研究旨在建立一种模型，从视觉背景中学习可分级形容词（如“大”，“小”）的含义，并通过关注完整目视文本，证明了多模型方法的有效性，在一系列任务中获得了建模尺寸含义的优异表现，但在开发可组合的抽象分级形容词表示方面仍然存在缺陷。

红场有多大？MALeViC：利用视觉环境建模形容词