Papers by Gerhard Hagerer
An Evaluation of Progressive Neural Networksfor Transfer Learning in Natural Language Processing (2020.lrec-1)
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Abdul Moeed, Gerhard Hagerer, Sumit Dugar, Sarthak Gupta, Mainak Ghosh, Hannah Danner, Oliver Mitevski, Andreas Nawroth, Georg Groh
| Challenge: | Fine-tuning suffers from catastrophic forgetting, a problem exacerbated in natural language processing (NLP). |
| Approach: | They propose to use progressive neural networks to re-use previously learned knowledge when learning new tasks. |
| Outcome: | The proposed approach improves on common NLP tasks across a range of architectures, datasets, and tasks. |
Evaluation Metrics for Headline Generation Using Deep Pre-Trained Embeddings (2020.lrec-1)
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| Challenge: | Recent generative language models have shown promise in abstractive summarization tasks. |
| Approach: | They propose to use Fr echet embedding distance and angular embeddable similarity to evaluate the performance of generative language models in abstractive summarization tasks. |
| Outcome: | The proposed metric shows close relation with human judgments and has overall better correlations with them. |