Multiple intent detection and slot filling are two fundamental and crucial tasks in spoken language understanding. Motivated by the fact that the two tasks are closely related, joint models that can detect intents and extract slots simultaneously are preferred to individual models that perform each task independently. The accuracy of a joint model depends heavily on the ability of the model to transfer information between the two tasks so that the result of one task can correct the result of the other. In addition, since a joint model has multiple outputs, how to train the model effectively is also challenging. In this paper, we present a method for multiple intent detection and slot filling by addressing these challenges. First, we propose a bidirectional joint model that explicitly employs intent information to recognize slots and slot features to detect intents. Second, we introduce a novel method for training the proposed joint model using supervised contrastive learning and self-distillation. Experimental results on two benchmark datasets MixATIS and MixSNIPS show that our method outperforms state-of-the-art models in both tasks. The results also demonstrate the contributions of both bidirectional design and the training method to the accuracy improvement. Our source code is available at https://github.com/anhtunguyen98/BiSLU

翻译：多意图检测与槽位填充是口语理解中两个基础且关键的任务。鉴于这两个任务密切关联，能够同时检测意图并提取槽位的联合模型优于独立执行每个任务的单任务模型。联合模型的准确性高度依赖于其在两个任务间传递信息的能力，使得一个任务的结果能够修正另一个任务的输出。此外，由于联合模型具有多个输出，如何有效训练该模型也颇具挑战性。本文通过解决这些挑战，提出了一种用于多意图检测与槽位填充的方法。首先，我们设计了一个双向联合模型，该模型显式利用意图信息识别槽位，并利用槽位特征检测意图。其次，我们引入了一种新颖的训练方法，通过监督对比学习与自蒸馏来训练所提出的联合模型。在两个基准数据集MixATIS和MixSNIPS上的实验结果表明，我们的方法在两个任务上均优于现有最优模型。实验结果还证明了双向设计及训练方法对准确性提升的贡献。我们的源代码可在https://github.com/anhtunguyen98/BiSLU获取。