基于S-LSTM模型利用‘槽值门’机制的说话人意图识别

Speaker intention recognition based on S-LSTM model and slot-gate

对说话人意图的识别极大地推进了自然语言理解任务的发展.之前的工作大多采用Bi-LSTM即双向LSTM模型进行词汇特征与词汇之间语义关系的提取,但这并不能很好地使句子整体和构成句子的词汇个体之间的信息进行交流.而S-LSTM(Sentence-state LSTM)模型,即句子状态LSTM模型可以很好地将自然语言中句子整体与词汇个体的信息相结合,以便于我们挖掘与利用意图检测与槽值填充之间的关系成立联合模型来更好地理解应答系统中蕴含的语义.因此,本文引入了‘槽值门’机制解决S-LSTM应用于意图检测与槽填充的联合任务时最新迭代时刻的句子状态信息没有得到充分利用的问题.最终的实验结果在ATIS数据集和Snips数据集上均取得了优于目前最先进算法的结果.

The recognition of the speaker s intention has greatly promoted the development of natural language understanding.In previous studies,the bidirectional long short-term memory(Bi-LSTM)model has been mostly employed in natural language processing to extract the features of words and the relationships between them.However,Bi-LSTM cannot establish a well-enough relation between the information contained in a sentence and its individual vocabulary.Another previously proposed model,i.e.,the S-LSTM(Sentence-state LSTM)model,can establish a relation between sentence information and its individual words.This,in turn,facilitates the establishment of the relationship between intention detection and slot filling,for the purpose of proposing a joint model to better understand the semantics contained in the question-answer system.Therefore,in this paper,slot-gate mechanism is introduced to solve the waste of the latest iteration sentence state when S-LSTM is applied to the joint task of intention detection and slot filling.The experimental results based on ATIS and Snips datasets confirm that the proposed mechanism is superior to other state-of-the-art approaches.