NLP tasks are often domain specific, yet systems can learn behaviors across multiple domains. We develop a new multi-domain online learning framework based on parameter combination from multiple classifiers. Our algorithms draw from multi-task learning and domain adaptation to adapt multiple source domain classifiers to a new target domain, learn across multiple similar domains, and learn across a large number of disparate domains. We evaluate our algorithms on two popular NLP domain adaptation tasks: sentiment classification and spam filtering.
REF proposed an online method for multi-domain learning and adaptation.
Online Methods for Multi-Domain Learning and Adaptation
Conference On Empirical Methods In Natural Language Processing
Computer Science