【摘要】 意图分类和槽位填充是口语理解任务的两个子任务，用于在对话系统中识别文本序列的意图以及从文本序列中提取出能进一步确定意图具体内容的槽位信息。近些年的研究表明，这两个任务具有相关性，且可以相互促进。然而，现有的大多数联合方法仅利用单一特征简单地通过共享参数建立两者之间的关联，而这往往会导致模型泛化能力差及特征利用程度低等问题。针对这些问题，提出了一种新的联合模型。模型在BERT的基础上引入了意图特征提取层和槽位特征提取层用于进一步提取文本特征，以增强文本向量表示能力，并通过多特征门机制融合了多方特征，充分利用两个任务之间的语义关系预测标签。在公开数据集ATIS和SNIPS上的实验结果表明，提出的模型能有效提升意图分类和槽位填充的性能，相比已有方法取得了更优的结果。更多还原

【Abstract】 Intent classification and slot filling are two subtasks of spoken language comprehension that are used to identify the intent of text sequences in a conversation system and to obtain slot information from the text sequences that may be used to further infer the exact substance of the intent.Recent research has revealed that these two tasks are connected and can reinforce one another.However,the majority of joint techniques now just use one feature to establish the relationship between the two by only exchanging parameters,which frequently results in issues like poor model generalization and low feature utilization.In order to solve these problems,a novel joint model is proposed that adds an intent feature extraction layer and a slot feature extraction layer for additional text feature extraction based on BERT to improve text vector representation.It also fuses features from different parties using gate mechanism to fully utilize the semantic relationship between the two tasks to predict labels.Experimental findings on the openly accessible datasets ATIS and SNIPS demonstrate the effectiveness of the proposed model in improving intent categorization and slot filling performance,outperforming current approaches.更多还原