A Unified Framework for Feature-based Domain Adaptation of Neural Network Language Models

Abstract

An important task for language models is the adaptation of general-domain models to specific target domains. For neural network-based language models, feature-based domain adaptation has been a popular method in previous research. Conventional methods use an adaptation feature providing context information that is calculated from a topic model. However, such a topic model needs to be trained separately from the language model. To unify the language and context model training, we present an approach that combines an extractor network and a domain adaptation layer. The extractor network learns a context representation from a fixed-size window of past words and provides the context information for the adaptation layer. The benefit of our method is that the extractor network can be trained jointly with the language model in a single training step. Our proposed method showed superior performance over conventional domain adaptation with topic features on a dataset of TED talks with respect to perplexity and word error rate after 100-best rescoring.