Character-level information is included in many NLP models, but evaluating the information encoded in character representations is an open issue.We leverage perceptual representations in the form of shape, sound, and color embeddings and perform a representational similarity analysis to evaluate their correlation with textual representations in five languages.This cross-lingual analysis shows that textual character representations correlate strongly with sound representations for languages using an alphabetic script, while shape correlates with featural scripts.We further develop a set of probing classifiers to intrinsically evaluate what phonological information is encoded in character embeddings.Our results suggest that information on features such as voicing are embedded in both LSTM and transformer-based representations.

While a great deal of work has been done on NLP approaches to lexical semantic change detection, other aspects of language change have received less attention from the NLP community. In this paper, we address the detection of sound change through historical spelling. We propose that a sound change can be captured by comparing the relative distance through time between the distributions of the characters involved before and after the change has taken place. We model these distributions using PPMI character embeddings. We verify this hypothesis in synthetic data and then test the method’s ability to trace the well-known historical change of lenition of plosives in Danish historical sources. We show that the models are able to identify several of the changes under consideration and to uncover meaningful contexts in which they appeared. The methodology has the potential to contribute to the study of open questions such as the relative chronology of sound shifts and their geographical distribution.