Papers by Benjamin Ruppik
A Confidence-based Acquisition Model for Self-supervised Active Learning and Label Correction (2025.tacl-1)
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Carel van Niekerk, Christian Geishauser, Michael Heck, Shutong Feng, Hsien-chin Lin, Nurul Lubis, Benjamin Ruppik, Renato Vukovic, Milica Gašić
| Challenge: | Existing approaches to training deep neural networks require large amounts of meticulously annotated data. |
| Approach: | They propose a pool-based active learning framework that requires expert annotators to label only a fraction of a sequence and facilitates self-supervision for the remainder of the sequence. |
| Outcome: | The proposed model outperforms baselines on dialogue belief tracking tasks. |
ChatGPT for Zero-shot Dialogue State Tracking: A Solution or an Opportunity? (2023.acl-short)
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Michael Heck, Nurul Lubis, Benjamin Ruppik, Renato Vukovic, Shutong Feng, Christian Geishauser, Hsien-chin Lin, Carel van Niekerk, Milica Gasic
| Challenge: | Recent research on dialog state tracking (DST) focuses on methods that allow few- and zero-shot transfer to new domains or schemas. |
| Approach: | They propose to use schema descriptions to facilitate zero-shot transfer to new domains . they argue that general purpose language models lack the ability to replace specialized systems . |
| Outcome: | The proposed method achieves state-of-the-art in zero-shot DST with in-context learning capabilities. |