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Found 7 papers, 4 papers with code
A Label-Aware BERT Attention Network for Zero-Shot Multi-Intent Detection in Spoken Language Understanding
We show that it successfully extends to few/zero-shot setting where part of intent labels are unseen in training data, by also taking account of semantics in these unseen intent labels.
Schema Graph-Guided Prompt for Multi-Domain Dialogue State Tracking
Tracking dialogue states is an essential topic in task-oriented dialogue systems, which involve filling in the necessary information in pre-defined slots corresponding to a schema.
CLICKER: Attention-Based Cross-Lingual Commonsense Knowledge Transfer
Recent advances in cross-lingual commonsense reasoning (CSR) are facilitated by the development of multilingual pre-trained models (mPTMs).
Choice Fusion as Knowledge for Zero-Shot Dialogue State Tracking
With the demanding need for deploying dialogue systems in new domains with less cost, zero-shot dialogue state tracking (DST), which tracks user's requirements in task-oriented dialogues without training on desired domains, draws attention increasingly.
Act-Aware Slot-Value Predicting in Multi-Domain Dialogue State Tracking
As an essential component in task-oriented dialogue systems, dialogue state tracking (DST) aims to track human-machine interactions and generate state representations for managing the dialogue.
Why patient data cannot be easily forgotten?
With the advent of AI learned on data, one can imagine that such rights can extent to requests for forgetting knowledge of patient's data within AI models.
A Context-Aware Hierarchical BERT Fusion Network for Multi-turn Dialog Act Detection
The success of interactive dialog systems is usually associated with the quality of the spoken language understanding (SLU) task, which mainly identifies the corresponding dialog acts and slot values in each turn.
