Compositionality in Computational Linguistics

Abstract

Neural models greatly outperform grammar-based models across many tasks in modern computational linguistics. This raises the question of whether linguistic principles, such as the Principle of Compositionality, still have value as modeling tools. We review the recent literature and find that while an overly strict interpretation of compositionality makes it hard to achieve broad coverage in semantic parsing tasks, compositionality is still necessary for a model to learn the correct linguistic generalizations from limited data. Reconciling both of these qualities requires the careful exploration of a novel design space; we also review some recent results that may help in this exploration.