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Found 5 papers, 0 papers with code
CAMELL: Confidence-based Acquisition Model for Efficient Self-supervised Active Learning with Label Validation
Supervised neural approaches are hindered by their dependence on large, meticulously annotated datasets, a requirement that is particularly cumbersome for sequential tasks.
From Chatter to Matter: Addressing Critical Steps of Emotion Recognition Learning in Task-oriented Dialogue
Our framework yields significant improvements for a range of chit-chat ERC models on EmoWOZ, a large-scale dataset for user emotion in ToDs.
EmoUS: Simulating User Emotions in Task-Oriented Dialogues
Existing user simulators (USs) for task-oriented dialogue systems only model user behaviour on semantic and natural language levels without considering the user persona and emotions.
ChatGPT for Zero-shot Dialogue State Tracking: A Solution or an Opportunity?
Recent research on dialogue state tracking (DST) focuses on methods that allow few- and zero-shot transfer to new domains or schemas.
Dialogue Term Extraction using Transfer Learning and Topological Data Analysis
Goal oriented dialogue systems were originally designed as a natural language interface to a fixed data-set of entities that users might inquire about, further described by domain, slots, and values.
