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Found 11 papers, 5 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.
Data Augmentation for Improving Tail-traffic Robustness in Skill-routing for Dialogue Systems
Large-scale conversational systems typically rely on a skill-routing component to route a user request to an appropriate skill and interpretation to serve the request.
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.
Knowledge Augmented BERT Mutual Network in Multi-turn Spoken Dialogues
Modern spoken language understanding (SLU) systems rely on sophisticated semantic notions revealed in single utterances to detect intents and slots.
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.
Longer Version for "Deep Context-Encoding Network for Retinal Image Captioning"
Automatically generating medical reports for retinal images is one of the promising ways to help ophthalmologists reduce their workload and improve work efficiency.
Ensemble-based Transfer Learning for Low-resource Machine Translation Quality Estimation
Quality Estimation (QE) of Machine Translation (MT) is a task to estimate the quality scores for given translation outputs from an unknown MT system.
Contextualized Keyword Representations for Multi-modal Retinal Image Captioning
A traditional medical image captioning model creates a medical description only based on a single medical image input.
DeepOpht: Medical Report Generation for Retinal Images via Deep Models and Visual Explanation
To train and validate the effectiveness of our DNN-based module, we propose a large-scale retinal disease image dataset.
