Open Intent Detection

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

17 leaderboard tables shown for this task, 17 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted. 10 shown of 17 until expanded.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

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.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

6 shown of 6 papers with code (6 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.

• TEXTOIR: An Integrated and Visualized Platform for Text Open Intent Recognition
  13 Sep 2021 2 repositories listed
  It is composed of two main modules: open intent detection and open intent discovery.
• ChatGPT as Data Augmentation for Compositional Generalization: A Case Study in Open Intent Detection
  25 Aug 2023 1 repository listed
  Open intent detection, a crucial aspect of natural language understanding, involves the identification of previously unseen intents in user-generated text.
• Metric Learning and Adaptive Boundary for Out-of-Domain Detection
  22 Apr 2022 1 repository listed
  Based on the open-world environment, we often encounter the situation that the training and test data are sampled from different distributions.
• Learning Discriminative Representations and Decision Boundaries for Open Intent Detection
  11 Mar 2022 1 repository listed
  To address these issues, this paper presents an original framework called DA-ADB, which successively learns distance-aware intent representations and adaptive decision boundaries for open intent detection.
• Deep Open Intent Classification with Adaptive Decision Boundary
  18 Dec 2020 1 repository listed
  In this paper, we propose a post-processing method to learn the adaptive decision boundary (ADB) for open intent classification.
• Deep Unknown Intent Detection with Margin Loss
  2 Jun 2019 1 repository listed Syntology ran 0 of 1 samples · 1 unverified · 1 pointer-only (licence)
  With margin loss, we can learn discriminative deep features by forcing the network to maximize inter-class variance and to minimize intra-class variance.

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.