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Found 4 papers, 1 papers with code
Granular Change Accuracy: A More Accurate Performance Metric for Dialogue State Tracking
Current metrics for evaluating Dialogue State Tracking (DST) systems exhibit three primary limitations.
CESAR: Automatic Induction of Compositional Instructions for Multi-turn Dialogs
We apply CESAR on InstructDial, a benchmark for instruction-based dialog tasks.
Prompter: Zero-shot Adaptive Prefixes for Dialogue State Tracking Domain Adaptation
A challenge in the Dialogue State Tracking (DST) field is adapting models to new domains without using any supervised data, zero-shot domain adaptation.
N-Shot Learning for Augmenting Task-Oriented Dialogue State Tracking
Augmentation of task-oriented dialogues has followed standard methods used for plain-text such as back-translation, word-level manipulation, and paraphrasing despite its richly annotated structure.
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