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Multimodal models have been proven to outperform text-based approaches onlearning semantic representations. However, it still remains unclear whatproperties are encoded in multimodal representations, in what aspects do theyoutperform the single-modality representations, and what happened in theprocess of semantic compositionality in different input modalities. Consideringthat multimodal models are originally motivated by human conceptrepresentations, we assume that correlating multimodal representations withbrain-based semantics would interpret their inner properties to answer theabove questions. To that end, we propose simple interpretation methods based onbrain-based componential semantics. First we investigate the inner propertiesof multimodal representations by correlating them with correspondingbrain-based property vectors. Then we map the distributed vector space to theinterpretable brain-based componential space to explore the inner properties ofsemantic compositionality. Ultimately, the present paper sheds light on thefundamental questions of natural language understanding, such as how torepresent the meaning of words and how to combine word meanings into largerunits.

Investigating Inner Properties of Multimodal Representation and Semantic Compositionality with Brain-based Componential Semantics