Montague semantics and modifier consistency measurement in neural language models

The recent dominance of distributional language representation models has elicited a variety of questions regarding their capabilities and intrinsic properties, one of which is the manifestation of compositional phenomena in natural language, which has significant implications towards explainability and safety/fairness in the use of such models. While most current research on compositionality has been directed towards improving performance of the representations on similarity tasks, this work proposes a methodology for measuring the presence of compositional behaviour in contemporary language models related to adjectival modifier phenomena in adjective-noun phrases. Our results show that current neural language models do not behave consistently according to the linguistic theories with regard to the evaluated intersective property, but on the other hand, the differences between adjective categories are noticeable in single adjective interactions, indicating that such differences are encoded in individual word representations, but they do not transfer generally in the expected way to the compositions. This raises the question of whether current language models are not capable of capturing the true underlying distributional properties of language, or whether linguistic theories from Montagovian tradition do not hold to distributional scrutiny.