Domain Adaptation for Neural Networks by Parameter Augmentation
This work addresses domain adaptation for neural networks in NLP, offering an incremental improvement over prior methods.
The paper tackles the problem of supervised domain adaptation for neural networks, particularly in NLP tasks like caption generation, by proposing a simple method based on parameter augmentation that improves performance over existing techniques on captioning datasets.
We propose a simple domain adaptation method for neural networks in a supervised setting. Supervised domain adaptation is a way of improving the generalization performance on the target domain by using the source domain dataset, assuming that both of the datasets are labeled. Recently, recurrent neural networks have been shown to be successful on a variety of NLP tasks such as caption generation; however, the existing domain adaptation techniques are limited to (1) tune the model parameters by the target dataset after the training by the source dataset, or (2) design the network to have dual output, one for the source domain and the other for the target domain. Reformulating the idea of the domain adaptation technique proposed by Daume (2007), we propose a simple domain adaptation method, which can be applied to neural networks trained with a cross-entropy loss. On captioning datasets, we show performance improvements over other domain adaptation methods.