Multi-task Multi-domain Representation Learning for Sequence Tagging.
Year: 2016
Type: preprint
Abstract: 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.
Source: arXiv (Cornell University)
Authors: Nanyun Peng, Mark Dredze
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2508857598