A Cross‐Modal and Cross‐lingual Study of Iconicity in Language: Insights From Deep Learning

The present paper addresses the study of non‐arbitrariness in language within a deep learning framework. We present a set of experiments aimed at assessing the pervasiveness of different forms of non‐arbitrary phonological patterns across a set of typologically distant languages. Different sequence‐processing neural networks are trained in a set of languages to associate the phonetic vectorization of a set of words to their sensory (Experiment 1), semantic (Experiment 2), and word‐class representations (Experiment 3). The models are then tested, without further training, in a set of novel instances in a language belonging to a different language family, and their performance is compared with a randomized baseline. We show that the three cross‐domain mappings can be successfully transferred across languages and language families, suggesting that the phonological structure of the lexicon is pervaded with language‐invariant cues about the words' meaning and their syntactic classes.