2 citations · 3 across the 5 of their papers we have counts for
5 papers
Towards Automated Error Discovery: A Study in Conversational AI
Dominic Petrak, Thy Thy Tran, Iryna Gurevych
Although LLM-based conversational agents demonstrate strong fluency and coherence, they still produce undesirable behaviors (errors) that are challenging to prevent from reaching u…
Learning from Implicit User Feedback, Emotions and Demographic Information in Task-Oriented and Document-Grounded Dialogues
Dominic Petrak, Thy Thy Tran, Iryna Gurevych
Implicit user feedback, user emotions and demographic information have shown to be promising sources for improving the accuracy and user engagement of responses generated by dialog…
Learning From Free-Text Human Feedback -- Collect New Datasets Or Extend Existing Ones?
Dominic Petrak, Nafise Sadat Moosavi, Ye Tian +2
Learning from free-text human feedback is essential for dialog systems, but annotated data is scarce and usually covers only a small fraction of error types known in conversational…
Lessons Learned from a Citizen Science Project for Natural Language Processing
Jan-Christoph Klie, Ji-Ung Lee, Kevin Stowe +6
Many Natural Language Processing (NLP) systems use annotated corpora for training and evaluation. However, labeled data is often costly to obtain and scaling annotation projects is…
Arithmetic-Based Pretraining -- Improving Numeracy of Pretrained Language Models
Dominic Petrak, Nafise Sadat Moosavi, Iryna Gurevych
State-of-the-art pretrained language models tend to perform below their capabilities when applied out-of-the-box on tasks that require understanding and working with numbers. Recen…