An essential component of any dialogue system is understanding the language which is known as spoken language understanding (SLU). Dialogue act classification (DAC), intent detection (ID) and slot filling (SF) are significant aspects of every dialogue system. In this paper, we propose a deep learning-based multi-task model that can perform DAC, ID and SF tasks together. We use a deep bi-directional recurrent neural network (RNN) with long short-term memory (LSTM) and gated recurrent unit (GRU) as the frameworks in our multi-task model. We use attention on the LSTM/GRU output for DAC and ID. The attention outputs are fed to individual task-specific dense layers for DAC and ID. The output of LSTM/GRU is fed to softmax layer for slot filling as well. Experiments on three datasets, i.e. ATIS, TRAINS and FRAMES, show that our proposed multi-task model performs better than the individual models as well as all the pipeline models. The experimental results prove that our attention-based multi-task model outperforms the state-of-the-art approaches for the SLU tasks. For DAC, in relation to the individual model, we achieve an improvement of more than 2% for all the datasets. Similarly, for ID, we get an improvement of 1% on the ATIS dataset, while for TRAINS and FRAMES dataset, there is a significant improvement of more than 3% compared to individual models. We also get a 0.8% enhancement for ATIS and a 4% enhancement for TRAINS and FRAMES dataset for SF with respect to individual models. Results obtained clearly show that our approach is better than existing methods. The validation of the obtained results is also demonstrated using statistical significance t tests.

任何对话系统的基本组成部分都是理解语言，这就是所谓的口语理解（SLU）。对话行为分类（DAC），意图检测（ID）和时隙填充（SF）是每个对话系统的重要方面。在本文中，我们提出了一种基于深度学习的多任务模型，该模型可以一起执行DAC，ID和SF任务。我们使用具有长短期记忆（LSTM）和门控循环单元（GRU）的深度双向循环神经网络（RNN）作为我们多任务模型的框架。我们关注DAC和ID的LSTM / GRU输出。注意输出被馈送到DAC和ID的各个任务特定的密集层。LSTM / GRU的输出也被馈送到softmax层以用于插槽填充。在三个数据集（ATIS，TRAINS和FRAMES）上进行实验，表明我们提出的多任务模型的性能优于单个模型以及所有流水线模型。实验结果证明，我们基于注意力的多任务模型优于SLU任务的最新方法。对于DAC，相对于单个模型，我们对所有数据集的改进均超过2％。同样，对于ID，我们在ATIS数据集上的改进为1％，而对于TRAINS和FRAMES数据集，与单个模型相比，改进了3％以上。相对于单个模型，ATIS的ATIS增强了0.8％，TRAINS和FRAMES数据集的增强了4％。获得的结果清楚地表明，我们的方法比现有方法更好。使用统计显着性也证明了所获得结果的有效性t测试。