Representation learning with deep models have demonstrated success in a range of NLP. In this paper we consider its use in a multi-task multi-domain setting for sequence tagging by proposing a unified framework for learning across tasks and domains. Our model learns robust representations that yield better performance in this setting. We use shared CRFs and domain projections to allow the model to learn domain specific representations that can feed a single task specific CRF. We evaluate our model on two tasks -Chinese word segmentation and named entity recognition -and two domains -news and social media -and achieve state-of-the-art results for both social media tasks.
REF adopt the model for Chinese segmentation and named entity recognition in the context of multi-task and multi-domain learning.
Multi-task Multi-domain Representation Learning for Sequence Tagging.
ArXiv
ArXiv
Computer Science