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Found 4 papers, 1 papers with code
Tri-level Joint Natural Language Understanding for Multi-turn Conversational Datasets
We present a novel tri-level joint natural language understanding approach, adding domain, and explicitly exchange semantic information between all levels.
Spoken Language Understanding for Conversational AI: Recent Advances and Future Direction
This tutorial will discuss how the joint task is set up and introduce Spoken Language Understanding/Natural Language Understanding (SLU/NLU) with Deep Learning techniques.
Bi-directional Joint Neural Networks for Intent Classification and Slot Filling
In this paper, we propose a bi-directional joint model for intent classification and slot filling, which includes a multi-stage hierarchical process via BERT and bi-directional joint natural language understanding mechanisms, including intent2slot and slot2intent, to obtain mutual performance enhancement between intent classification and slot filling.
CONDA: a CONtextual Dual-Annotated dataset for in-game toxicity understanding and detection
Accompanying the dataset is a thorough in-game toxicity analysis, which provides comprehensive understanding of context at utterance, token, and dual levels.
