  Ambiguity is an critical component of language that allows for more effective
communication between speakers, but is often ignored in NLP. Recent work
suggests that NLP systems may struggle to grasp certain elements of human
language understanding because they may not handle ambiguities at the level
that humans naturally do in communication. Additionally, different types of
ambiguity may serve different purposes and require different approaches for
resolution, and we aim to investigate how language models' abilities vary
across types. We propose a taxonomy of ambiguity types as seen in English to
facilitate NLP analysis. Our taxonomy can help make meaningful splits in
language ambiguity data, allowing for more fine-grained assessments of both
datasets and model performance.
