ENHANCE RNNLMS WITH HIERARCHICAL MULTI-TASK LEARNING FOR ASR

It is known that neural language models (NLMs) can implicitly learn certain linguistic information from text. While generally NLMs only use word feature input, the success of factored NLMs has indicated a benefit of using additional linguistic feature inputs for language modeling. On the other hand, multi-task learning (MTL) has shown positive effects on the generalization performance of various natural language processing (NLP) tasks, including language modeling. However, how to best share information among related tasks in MTL remains to be addressed. In this current work, we propose a hierarchical multi-task learning (HMTL) approach to incorporate linguistic knowledge into recurrent neural network language models (RNNLM), instead of using linguistic features as word factors. Specifically, we consider the auxiliary tasks of chunking, part of speech tagging, and named entity recognition, and supervise the learning of these auxiliary tasks in a hierarchical way. Our proposed method has the potential of helping language models learn knowledge of linguistic hierarchy from the auxiliary tasks, and improve the performance of RNNLMs on automatic speech recognition (ASR). We have evaluated our proposed HMTL method on WSJ and AMI speech recognition tasks. Our experiment results demonstrate the effectiveness of the proposed approach.