在当前自然语言处理多意图识别模型研究中, 存在建模方式均为从意图到插槽的单一方向的信息流建模, 忽视了插槽到意图的信息流交互建模研究, 意图识别任务易于混淆且错误捕获其他意图信息, 上下文语义特征提取质量不佳, 有待进一步提升等问题. 本文以当前先进的典型代表GL-GIN模型为基础, 进行优化改进, 探索了插槽到意图的交互建模方法, 运用槽到意图的单向注意力层, 计算插槽到意图的注意力得分, 纳入注意力机制, 利用插槽到意图的注意力得分作为连接权重, 使其可以传播和聚集与意图相关的插槽信息, 使意图重点关注与其相关的插槽信息, 从而实现多意图识别模型的双向信息流动; 同时, 引入BERT模型作为编码层, 以提升了语义特征提取质量. 实验表明, 该交互建模方法效果提升明显, 与原GL-GIN模型相比, 在两个公共数据集(MixATIS和MixSNIPS)上, 新模型的总准确率分别提高了5.2%和9%.
In the current research on multi-intention recognition models of natural language processing, information flow is only modeled from intention to slot, and the research on the interactive modeling of information flow from slot to intention is ignored. In addition, the task of intention recognition is easy to be confused, and other intention information is wrongly captured. The quality of contextual semantic feature extraction is poor and needs to be improved. In order to solve these problems, this study optimizes the current advanced typical GL-GIN (global-locally graph interaction network) model, explores the interactive modeling method from slot to intention, and uses the one-way attention layer from slot to intention. Furthermore, the study calculates the attention score from slot to intention, incorporates the attention mechanism, and uses the attention score from slot to intention as the connection weight. As a result, it can propagate and gather intention-related slot information and make the intention focus on the slot information that is relevant to it, so as to realize the bidirectional information flow of the multi-intention recognition model. At the same time, the BERT model is introduced as the coding layer to improve the quality of semantic feature extraction. Experiments show that the effect of this interactive modeling method is significantly improved. Compared with that of the original GL-GIN model, the overall accuracy of the new model on two public datasets (MixATIS and MixSNIPS) is increased by 5.2% and 9%, respectively.