Papers by Johannes Mario Meissner
Debiasing Masks: A New Framework for Shortcut Mitigation in NLU (2022.emnlp-main)
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| Challenge: | Debiasing language models from unwanted behaviors in natural language understanding datasets is a topic with increasing interest in the NLP community. |
| Approach: | They propose a method to debiase language models from unwanted behaviors in NLU tasks by identifying pruning masks that can be applied to a finetuned model. |
| Outcome: | The proposed method shows superior performance and performance over standard methods. |
Embracing Ambiguity: Shifting the Training Target of NLI Models (2021.acl-short)
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| Challenge: | Previously, it was common to disregard ambiguity as noise or as a sign of poor quality data. |
| Approach: | They propose to train on the estimated label distribution of annotators in a NLI task . they use a learning loss based on this ambiguity distribution instead of gold-labels . |
| Outcome: | The proposed training method reduces divergence scores on a trial dataset . the study shows that targeting the ambiguity distribution can improve performance . |