Browse State-of-the-Art › Intent Classification and Slot Filling
Intent Classification and Slot Filling
4 papers with code · 0 benchmarks · 3 datasets archive 2025-07-28
Benchmarks archive 2025-07-28
No benchmark for this task in the archive.
Libraries
Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.
Datasets archive 2025-07-28
3 datasets whose archive record lists this task, ordered by the archive's paper count.
Subtasks archive 2025-07-28
No subtask under this task in the archive's task tree.
Most implemented papers archive 2025-07-28
4 shown of 4 papers with code (33 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.
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13 Apr 2023 4 repositories listed Syntology ran 1 of 2 samples · 1 unverifiedTDT models for Speech Recognition achieve better accuracy and up to 2.
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28 Mar 2024 1 repository listedA combination ofmultiple datasets, including the MEDIA dataset, was suggested for training this joint model.
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21 Sep 2022 1 repository listedIn the success of the pre-trained BERT model, NLU is addressed by Intent Classification and Slot Filling task with significant improvement performance.
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5 Apr 2021 1 repository listedIntent detection and slot filling are important tasks in spoken and natural language understanding.
Syntology lines on 1 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.
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