Papers by Piotr Czapla
MultiFiT: Efficient Multi-lingual Language Model Fine-tuning (D19-1)
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| Challenge: | Pretrained language models require unlabelled data for training, while cross-lingual models underperform on low-resource languages. |
| Approach: | They propose a multi-lingual language model fine-tuning to train and fine- tune language models efficiently in their own language. |
| Outcome: | The proposed method outperforms existing models on two widely used datasets on cross-lingual classification tasks. |
AxCell: Automatic Extraction of Results from Machine Learning Papers (2020.emnlp-main)
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Marcin Kardas, Piotr Czapla, Pontus Stenetorp, Sebastian Ruder, Sebastian Riedel, Ross Taylor, Robert Stojnic
| Challenge: | AxCell is an automatic machine learning pipeline for extracting results from papers . it uses a table segmentation subtask to learn relevant structural knowledge that aids extraction. |
| Approach: | They propose to use a table segmentation subtask to learn relevant structural knowledge that aids extraction. |
| Outcome: | The proposed approach improves state of the art for results extraction and can be used for semi-automated results extraction in production. |