A Taxonomy of Ambiguity Types for NLP
This work addresses the challenge of improving NLP systems' ability to handle language ambiguity, which is often overlooked but critical for human-like understanding, though it appears incremental as it focuses on categorization rather than novel resolution methods.
The paper tackles the problem of ambiguity in NLP by proposing a taxonomy of ambiguity types in English to enable more fine-grained analysis of datasets and model performance, without providing specific numerical results.
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.