  Elastic Weight Consolidation (EWC) is a technique used in overcoming
catastrophic forgetting between successive tasks trained on a neural network.
We use this phenomenon of information sharing between tasks for domain
adaptation. Training data for tasks such as sentiment analysis (SA) may not be
fairly represented across multiple domains. Domain Adaptation (DA) aims to
build algorithms that leverage information from source domains to facilitate
performance on an unseen target domain. We propose a model-independent
framework - Sequential Domain Adaptation (SDA). SDA draws on EWC for training
on successive source domains to move towards a general domain solution, thereby
solving the problem of domain adaptation. We test SDA on convolutional,
recurrent, and attention-based architectures. Our experiments show that the
proposed framework enables simple architectures such as CNNs to outperform
complex state-of-the-art models in domain adaptation of SA. In addition, we
observe that the effectiveness of a harder first Anti-Curriculum ordering of
source domains leads to maximum performance.
