In natural language processing (NLP) annotation projects, we use inter-annotator agreement measures and annotation guidelines to ensure consistent annotations. However, annotation guidelines often make linguistically debatable and even somewhat arbitrary decisions, and interannotator agreement is often less than perfect. While annotation projects usually specify how to deal with linguistically debatable phenomena, annotator disagreements typically still stem from these "hard" cases. This indicates that some errors are more debatable than others. In this paper, we use small samples of doublyannotated part-of-speech (POS) data for Twitter to estimate annotation reliability and show how those metrics of likely interannotator agreement can be implemented in the loss functions of POS taggers. We find that these cost-sensitive algorithms perform better across annotation projects and, more surprisingly, even on data annotated according to the same guidelines. Finally, we show that POS tagging models sensitive to inter-annotator agreement perform better on the downstream task of chunking.
REF incorporate annotator disagreement in POS tags into the loss function of a POS-tag machine learner, resulting in improved performance on downstream chunking.
Learning part-of-speech taggers with inter-annotator agreement loss
EACL
Computer Science