Papers › Joint Multiple Intent Detection and Slot Filling with Supervised Contrastive Learning...

Joint Multiple Intent Detection and Slot Filling with Supervised Contrastive Learning and Self-Distillation

28 Aug 2023arXiv:2308.14654archive 2025-07-28

Nguyen Anh Tu, Hoang Thi Thu Uyen, Tu Minh Phuong, Ngo Xuan Bach

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

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Code

anhtunguyen98/bislu officialmentioned in paperpytorch report

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Tasks

Contrastive LearningIntent DetectionSemantic Frame ParsingSlot FillingSpoken Language Understandingslot-filling

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Intent Detection MixATIS BiSLU Accuracy 81.5 #5 of 15 Archive leaderboard report
Intent Detection MixSNIPS BiSLU Accuracy 97.8 #2 of 16 Archive leaderboard report
Slot Filling MixATIS BiSLU Micro F1 89.4 #3 of 15 Archive leaderboard report
Slot Filling MixSNIPS BiSLU Micro F1 97.2 #1 of 16 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Contrastive Learning

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