Semantic Frame Parsing
5 papers with code • 3 benchmarks • 2 datasets
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
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.
GL-GIN: Fast and Accurate Non-Autoregressive Model for Joint Multiple Intent Detection and Slot Filling
Multi-intent SLU can handle multiple intents in an utterance, which has attracted increasing attention.
Text is no more Enough! A Benchmark for Profile-based Spoken Language Understanding
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).
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.