  Recent research efforts have shown that neural architectures can be effective
in conventional information extraction tasks such as named entity recognition,
yielding state-of-the-art results on standard newswire datasets. However,
despite significant resources required for training such models, the
performance of a model trained on one domain typically degrades dramatically
when applied to a different domain, yet extracting entities from new emerging
domains such as social media can be of significant interest. In this paper, we
empirically investigate effective methods for conveniently adapting an
existing, well-trained neural NER model for a new domain. Unlike existing
approaches, we propose lightweight yet effective methods for performing domain
adaptation for neural models. Specifically, we introduce adaptation layers on
top of existing neural architectures, where no re-training using the source
domain data is required. We conduct extensive empirical studies and show that
our approach significantly outperforms state-of-the-art methods.

Computation and Language