Marrying Up Regular Expressions with Neural Networks: A Case Study for Spoken Language Understanding
Marrying Up Regular Expressions with Neural Networks: A Case Study for Spoken Language Understanding
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DOI：
10.18653/v1/p18-1194
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发表时间：
2018-05
期刊：
影响因子：
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通讯作者：
Bingfeng Luo;Yansong Feng;Zheng Wang;Songfang Huang;Rui Yan;Dongyan Zhao
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文献类型：
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作者：
Bingfeng Luo;Yansong Feng;Zheng Wang;Songfang Huang;Rui Yan;Dongyan Zhao
许多自然语言处理（NLP）任务的成功取决于注释数据的数量和质量，但通常缺乏此类训练数据。在本文中，我们提出了这样一个问题：“我们能否将联合收割机神经网络（NN）与正则表达式（RE）结合起来，以改进NLP的监督学习？"。在回答中，我们开发了新的方法来利用丰富的表现力的RE在不同级别的NN，显示出的组合显着提高了学习效率时，少量的训练样本。我们评估我们的方法，将其应用到口语理解的意图检测和插槽填充。实验结果表明，我们的方法是非常有效的利用现有的训练数据，给了一个明确的推动RE不知道神经网络。
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.