Abstract
In this paper, we focus on named entity boundary
detection, which aims to detect the start and end
boundaries of an entity mention in text, without
predicting its type. A more accurate and robust detection approach is desired to alleviate error propagation in downstream applications, such as entity
linking and fine-grained typing systems. Here, we
first develop a novel entity boundary labeling approach with pointer networks, where the output dictionary size depends on the input, which is variable.
Furthermore, we propose AT-BDRY, which incorporates adversarial transfer learning into an end-toend sequence labeling model to encourage domaininvariant representations. More importantly, ATBDRY can reduce domain difference in data distributions between the source and target domains,
via an unsupervised transfer learning approach (i.e.,
no annotated target-domain data is necessary). We
conduct Formal Text ? Formal Text, Formal Text
? Informal Text and ablation evaluations on five
benchmark datasets. Experimental results show
that AT-BDRY achieves state-of-the-art transferring
performance against recent baselines