  In general, the labels used in sequence labeling consist of different types
of elements. For example, IOB-format entity labels, such as B-Person and
I-Person, can be decomposed into span (B and I) and type information (Person).
However, while most sequence labeling models do not consider such label
components, the shared components across labels, such as Person, can be
beneficial for label prediction. In this work, we propose to integrate label
component information as embeddings into models. Through experiments on English
and Japanese fine-grained named entity recognition, we demonstrate that the
proposed method improves performance, especially for instances with
low-frequency labels.
