CAMELL: Confidence-based Acquisition Model for Efficient Self-supervised Active Learning with Label Validation

no code implementations • 13 Oct 2023 • Carel van Niekerk, Christian Geishauser, Michael Heck, Shutong Feng, Hsien-Chin Lin, Nurul Lubis, Benjamin Ruppik, Renato Vukovic, Milica Gašić

Supervised neural approaches are hindered by their dependence on large, meticulously annotated datasets, a requirement that is particularly cumbersome for sequential tasks.

Active Learning

From Chatter to Matter: Addressing Critical Steps of Emotion Recognition Learning in Task-oriented Dialogue

no code implementations • 24 Aug 2023 • Shutong Feng, Nurul Lubis, Benjamin Ruppik, Christian Geishauser, Michael Heck, Hsien-Chin Lin, Carel van Niekerk, Renato Vukovic, Milica Gašić

Our framework yields significant improvements for a range of chit-chat ERC models on EmoWOZ, a large-scale dataset for user emotion in ToDs.

Emotion Recognition Multi-Task Learning

EmoUS: Simulating User Emotions in Task-Oriented Dialogues

no code implementations • 2 Jun 2023 • Hsien-Chin Lin, Shutong Feng, Christian Geishauser, Nurul Lubis, Carel van Niekerk, Michael Heck, Benjamin Ruppik, Renato Vukovic, Milica Gašić

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.

Language Modelling Large Language Model +1

ChatGPT for Zero-shot Dialogue State Tracking: A Solution or an Opportunity?

no code implementations • 2 Jun 2023 • Michael Heck, Nurul Lubis, Benjamin Ruppik, Renato Vukovic, Shutong Feng, Christian Geishauser, Hsien-Chin Lin, Carel van Niekerk, Milica Gašić

Recent research on dialogue state tracking (DST) focuses on methods that allow few- and zero-shot transfer to new domains or schemas.

Data Augmentation Dialogue State Tracking +2