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Grounding has been argued to be a crucial component towards the developmentof more complete and truly semantically competent artificial intelligencesystems. Literature has divided into two camps: While some argue that groundingallows for qualitatively different generalizations, others believe it can becompensated by mono-modal data quantity. Limited empirical evidence has emergedfor or against either position, which we argue is due to the methodologicalchallenges that come with studying grounding and its effects on NLP systems.  In this paper, we establish a methodological framework for studying what theeffects are - if any - of providing models with richer input sources thantext-only. The crux of it lies in the construction of comparable samples ofpopulations of models trained on different input modalities, so that we cantease apart the qualitative effects of different input sources fromquantifiable model performances. Experiments using this framework revealqualitative differences in model behavior between cross-modally grounded,cross-lingually grounded, and ungrounded models, which we measure both at aglobal dataset level as well as for specific word representations, depending onhow concrete their semantics is.

Grounded and Well-rounded: A Methodological Approach to the Study of Cross-modal and Cross-lingual Grounding