Progressive Memory Banks for Incremental Domain Adaptation
It addresses the problem of adapting models to new domains over time without forgetting old ones, which is incremental but improves robustness and performance in NLP applications.
This paper tackles incremental domain adaptation in NLP by augmenting RNNs with a parameterized memory bank that expands with new domains, achieving significantly better performance than fine-tuning alone and outperforming previous methods like elastic weight consolidation and progressive neural networks.
This paper addresses the problem of incremental domain adaptation (IDA) in natural language processing (NLP). We assume each domain comes one after another, and that we could only access data in the current domain. The goal of IDA is to build a unified model performing well on all the domains that we have encountered. We adopt the recurrent neural network (RNN) widely used in NLP, but augment it with a directly parameterized memory bank, which is retrieved by an attention mechanism at each step of RNN transition. The memory bank provides a natural way of IDA: when adapting our model to a new domain, we progressively add new slots to the memory bank, which increases the number of parameters, and thus the model capacity. We learn the new memory slots and fine-tune existing parameters by back-propagation. Experimental results show that our approach achieves significantly better performance than fine-tuning alone. Compared with expanding hidden states, our approach is more robust for old domains, shown by both empirical and theoretical results. Our model also outperforms previous work of IDA including elastic weight consolidation and progressive neural networks in the experiments.