de Varda AG, Strapparava C. A Cross-Modal and Cross-lingual Study of Iconicity in Language: Insights From Deep Learning.
Cogn Sci 2022;
46:e13147. [PMID:
35665953 PMCID:
PMC9285447 DOI:
10.1111/cogs.13147]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Revised: 04/27/2022] [Accepted: 04/28/2022] [Indexed: 11/30/2022]
Abstract
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.
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