Marrying up Regular Expressions with Neural Networks: A Case Study for Spoken Language Understanding
This work addresses data scarcity in NLP for applications like intent detection and slot filling, presenting an incremental improvement by integrating existing techniques.
The paper tackles the problem of limited annotated data in NLP by combining neural networks with regular expressions to improve learning effectiveness, achieving a clear performance boost over neural networks alone in spoken language understanding tasks.
The success of many natural language processing (NLP) tasks is bound by the number and quality of annotated data, but there is often a shortage of such training data. In this paper, we ask the question: "Can we combine a neural network (NN) with regular expressions (RE) to improve supervised learning for NLP?". In answer, we develop novel methods to exploit the rich expressiveness of REs at different levels within a NN, showing that the combination significantly enhances the learning effectiveness when a small number of training examples are available. We evaluate our approach by applying it to spoken language understanding for intent detection and slot filling. Experimental results show that our approach is highly effective in exploiting the available training data, giving a clear boost to the RE-unaware NN.