Neural Adaptation Layers for Cross-domain Named Entity Recognition
This addresses the practical problem of efficiently adapting NER models to new domains for researchers and practitioners, though it appears incremental as it builds on existing neural architectures.
The paper tackles the problem of domain adaptation for neural named entity recognition models, where performance degrades when applied to new domains like social media, and proposes lightweight adaptation layers that significantly outperform state-of-the-art methods without requiring retraining on source domain data.
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.