Semantic Frame Parsing
11 papers with code • 3 benchmarks • 3 datasets
MixATIS
Most implemented papers
SLING: A framework for frame semantic parsing
We describe SLING, a framework for parsing natural language into semantic frames.
A Bi-model based RNN Semantic Frame Parsing Model for Intent Detection and Slot Filling
ray075hl/Bi-Model-Intent-And-Slot
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NAACL 2018
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.
AGIF: An Adaptive Graph-Interactive Framework for Joint Multiple Intent Detection and Slot Filling
LooperXX/AGIF
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Findings of the Association for Computational Linguistics 2020
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 slot prediction.
GL-GIN: Fast and Accurate Non-Autoregressive Model for Joint Multiple Intent Detection and Slot Filling
yizhen20133868/GL-GIN
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ACL 2021
Multi-intent SLU can handle multiple intents in an utterance, which has attracted increasing attention.
SLIM: Explicit Slot-Intent Mapping with BERT for Joint Multi-Intent Detection and Slot Filling
TRUMANCFY/SLIM
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Utterance-level intent detection and token-level slot filling are two key tasks for natural language understanding (NLU) in task-oriented systems.
Text is no more Enough! A Benchmark for Profile-based Spoken Language Understanding
looperxx/proslu
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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. g., intent and slots).
Co-guiding Net: Achieving Mutual Guidances between Multiple Intent Detection and Slot Filling via Heterogeneous Semantics-Label Graphs
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.
Enhancing Joint Multiple Intent Detection and Slot Filling with Global Intent-Slot Co-occurrence
smxiao/GISCo
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Conference on Empirical Methods in Natural Language Processing 2022
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.
Joint Multiple Intent Detection and Slot Filling with Supervised Contrastive Learning and Self-Distillation
The results also demonstrate the contributions of both bidirectional design and the training method to the accuracy improvement.
MISCA: A Joint Model for Multiple Intent Detection and Slot Filling with Intent-Slot Co-Attention
The research study of detecting multiple intents and filling slots is becoming more popular because of its relevance to complicated real-world situations.
