Papers by Luisa März
KnowMAN: Weakly Supervised Multinomial Adversarial Networks (2021.emnlp-main)
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| Challenge: | Existing approaches to weakly supervised training lack labeled data . weakly-supervised training can result in heuristic but noisy labels . |
| Approach: | They propose a scheme that allows to control influence of signals associated with specific labeling functions. |
| Outcome: | The proposed scheme improves results compared to weakly supervised learning with a pre-trained transformer language model and a feature-based baseline. |
Domain adaptation for part-of-speech tagging of noisy user-generated text (N19-1)
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| Challenge: | Existing POS taggers for canonical German text achieve good results around 97% accuracy, but when applying these trained models to out-of-domain data the performance decreases drastically. |
| Approach: | They propose a neural network that trains an out-of-domain model on a large newswire corpus and transfers those weights by using them as a prior for a model trained on the target domain. |
| Outcome: | The proposed model achieves a tagging accuracy of slightly over 90%, improving on the previous state of the art for this task. |