Semantic Frame Parsing

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

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Most implemented papers archive 2025-07-28

11 shown of 11 papers with code (23 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.

• SLING: A framework for frame semantic parsing
  19 Oct 2017 2 repositories listed Syntology ran 1 of 5 samples · 4 unverified
  We describe SLING, a framework for parsing natural language into semantic frames.
• MISCA: A Joint Model for Multiple Intent Detection and Slot Filling with Intent-Slot Co-Attention
  10 Dec 2023 1 repository listed
  The research study of detecting multiple intents and filling slots is becoming more popular because of its relevance to complicated real-world situations.
• Joint Multiple Intent Detection and Slot Filling with Supervised Contrastive Learning and Self-Distillation
  28 Aug 2023 1 repository listed
  The results also demonstrate the contributions of both bidirectional design and the training method to the accuracy improvement.
• Enhancing Joint Multiple Intent Detection and Slot Filling with Global Intent-Slot Co-occurrence
  1 Dec 2022 1 repository listed
  To be specific, an intent-slot co-occurrence graph is constructed based on the entire training corpus to globally discover correlation between intents and slots.
• Co-guiding Net: Achieving Mutual Guidances between Multiple Intent Detection and Slot Filling via Heterogeneous Semantics-Label Graphs
  19 Oct 2022 1 repository listed
  In this paper, we propose a novel model termed Co-guiding Net, which implements a two-stage framework achieving the \textit{mutual guidances} between the two tasks.
• Incorporating Instructional Prompts into a Unified Generative Framework for Joint Multiple Intent Detection and Slot Filling
  1 Oct 2022 1 repository listed
  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…
• Text is no more Enough! A Benchmark for Profile-based Spoken Language Understanding
  22 Dec 2021 1 repository listed
  Current researches on spoken language understanding (SLU) heavily are limited to a simple setting: the plain text-based SLU that takes the user utterance as input and generates its corresponding semantic frames (e.
• SLIM: Explicit Slot-Intent Mapping with BERT for Joint Multi-Intent Detection and Slot Filling
  26 Aug 2021 1 repository listed
  Utterance-level intent detection and token-level slot filling are two key tasks for natural language understanding (NLU) in task-oriented systems.
• GL-GIN: Fast and Accurate Non-Autoregressive Model for Joint Multiple Intent Detection and Slot Filling
  3 Jun 2021 1 repository listed
  Multi-intent SLU can handle multiple intents in an utterance, which has attracted increasing attention.
• AGIF: An Adaptive Graph-Interactive Framework for Joint Multiple Intent Detection and Slot Filling
  21 Apr 2020 1 repository listed
  Such an interaction layer is applied to each token adaptively, which has the advantage to automatically extract the relevant intents information, making a fine-grained intent information integration for the token-level…
• A Bi-model based RNN Semantic Frame Parsing Model for Intent Detection and Slot Filling
  26 Dec 2018 1 repository listed
  The most effective algorithms are based on the structures of sequence to sequence models (or "encoder-decoder" models), and generate the intents and semantic tags either using separate models or a joint model.

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.