Exploring Meaning Encoded in Random Character Sequences with Character-Aware Language Models

Anonymous

Abstract: Natural language processing models learn word representations based on the distributional hypothesis, which asserts that word context (e.g., co-occurrence) correlates with semantic meaning. We propose that n-grams composed of random character sequences, or garble, provide a novel context for studying word meaning both within and beyond extant language. In particular, randomly-generated character n-grams lack semantic meaning but contain primitive information based on the distribution of characters they contain. By studying the embeddings of a large corpus of garble, extant language, and pseudowords using CharacterBERT, we identify an axis in the model's high-dimensional embedding space that separates these classes of n-grams. Furthermore, we show that this axis relates to structure within extant language, including word part of speech, morphology, and concreteness. Thus, in contrast to studies that are mainly limited to extant language, our work reveals that semantic meaning and primitive information are intrinsically linked.