Papers by Wencke Liermann
More Insightful Feedback for Tutoring: Enhancing Generation Mechanisms and Automatic Evaluation (2024.emnlp-main)
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| Challenge: | Incorrect student answers can be valuable learning opportunities provided that the student understands where they went wrong and why. |
| Approach: | They propose to use a KL regularization term to achieve more targeted input representations and a preference optimization step to encourage student answer-adaptive feedback generation. |
| Outcome: | The proposed model outperforms existing models in 3.3 METEOR points. |
Dialogue Act-Aided Backchannel Prediction Using Multi-Task Learning (2023.findings-emnlp)
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| Challenge: | Backchanneling is a form of feedback that is produced by listeners in a conversation . since the advent of ChatGPT, modern dialogue systems exhibit answer quality levels on par with humans in various professions. |
| Approach: | They propose a multi-task learning approach that learns textual representations for the task of backchannel prediction in tandem with dialogue act classification. |
| Outcome: | The proposed approach improves the prediction of specific backchannels by up to 2.0% in F1 . the audio encoder is pre-trained in a self-supervised fashion using voice activity projection . |
The slurk Interaction Server Framework: Better Data for Better Dialog Models (2022.lrec-1)
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| Challenge: | slurk is a lightweight dialog data collection and testing tool for crowdsourcing platforms. |
| Approach: | They present a lightweight dialog server that allows to set up dialog data collections and run experiments. |
| Outcome: | The slurk software allows to set up dialog data collections and run experiments with no limitations on the number of participants. |
Improving Backchannel Prediction Leveraging Sequential and Attentive Context Awareness (2024.findings-eacl)
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| Challenge: | Backchannels are short and often affirmative or empathetic responses from a listener during a conversation . et al. (2010) showed that timely backchanneling can enhance storytelling ability . |
| Approach: | They propose a context-aware backchannel prediction approach that leverages a pretrained wav2vec model to enhance backchannel performance. |
| Outcome: | The proposed approach improves performance in Korean and English datasets . it leverages the pretrained wav2vec model for encoding audio signal . |