联合多意图检测与槽填充：基于监督对比学习和自蒸馏
Joint Multiple Intent Detection and Slot Filling with Supervised Contrastive Learning and Self-Distillation
AI总结：
本文提出双向联合模型，结合监督对比学习和自蒸馏，解决多意图检测与槽填充的联合训练难题，在MixATIS和MixSNIPS上超越现有最佳模型。
摘要：
多意图检测和槽填充是口语理解中两个基础且关键的任务。由于这两个任务密切相关，能够同时检测意图和提取槽的联合模型优于独立执行每个任务的单独模型。联合模型的准确性在很大程度上取决于模型在两个任务之间传递信息的能力，从而使一个任务的结果能够纠正另一个任务的结果。此外，由于联合模型具有多个输出，如何有效训练该模型也具有挑战性。在本文中，我们提出了一种通过解决这些挑战来进行多意图检测和槽填充的方法。首先，我们提出了一个双向联合模型，该模型明确利用意图信息来识别槽，并利用槽特征来检测意图。其次，我们介绍了一种使用监督对比学习和自蒸馏来训练所提出的联合模型的新方法。在两个基准数据集MixATIS和MixSNIPS上的实验结果表明，我们的方法在这两个任务上都优于最先进的模型。结果还证明了双向设计和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。
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
