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Incorporating Instructional Prompts into a Unified Generative Framework for Joint Multiple Intent Detection and Slot Filling

1 Oct 2022COLING 2022 10archive 2025-07-28

Yangjun Wu, Han Wang, Dongxiang Zhang, Gang Chen, Hao Zhang

The joint multiple Intent Detection (ID) and Slot Filling (SF) is a significant challenge in spoken language understanding. Because the slots in an utterance may relate to multi-intents, most existing approaches focus on utilizing task-specific components to capture the relations between intents and slots. The customized networks restrict models from modeling commonalities between tasks and generalization for broader applications. To address the above issue, we propose a Unified Generative framework (UGEN) based on a prompt-based paradigm, and formulate the task as a question-answering problem. Specifically, we design 5-type templates as instructional prompts, and each template includes a question that acts as the driver to teach UGEN to grasp the paradigm, options that list the candidate intents or slots to reduce the answer search space, and the context denotes original utterance. Through the instructional prompts, UGEN is guided to understand intents, slots, and their implicit correlations. On two popular multi-intent benchmark datasets, experimental results demonstrate that UGEN achieves new SOTA performances on full-data and surpasses the baselines by a large margin on 5-shot (28.1%) and 10-shot (23%) scenarios, which verify that UGEN is robust and effective.

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Tasks

Intent DetectionSemantic Frame ParsingSlot FillingSpoken Language Understandingslot-filling

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Intent Detection MixATIS UGEN Accuracy 83.0 #2 of 15 Archive leaderboard report
Intent Detection MixSNIPS UGEN Accuracy 96.9 #11 of 16 Archive leaderboard report
Slot Filling MixATIS UGEN Micro F1 89.2 #5 of 15 Archive leaderboard report
Slot Filling MixSNIPS UGEN Micro F1 95.0 #13 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.

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