Piotr Czapla


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AxCell: Automatic Extraction of Results from Machine Learning Papers
Marcin Kardas | Piotr Czapla | Pontus Stenetorp | Sebastian Ruder | Sebastian Riedel | Ross Taylor | Robert Stojnic
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Tracking progress in machine learning has become increasingly difficult with the recent explosion in the number of papers. In this paper, we present AxCell, an automatic machine learning pipeline for extracting results from papers. AxCell uses several novel components, including a table segmentation subtask, to learn relevant structural knowledge that aids extraction. When compared with existing methods, our approach significantly improves the state of the art for results extraction. We also release a structured, annotated dataset for training models for results extraction, and a dataset for evaluating the performance of models on this task. Lastly, we show the viability of our approach enables it to be used for semi-automated results extraction in production, suggesting our improvements make this task practically viable for the first time. Code is available on GitHub.


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MultiFiT: Efficient Multi-lingual Language Model Fine-tuning
Julian Eisenschlos | Sebastian Ruder | Piotr Czapla | Marcin Kadras | Sylvain Gugger | Jeremy Howard
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Pretrained language models are promising particularly for low-resource languages as they only require unlabelled data. However, training existing models requires huge amounts of compute, while pretrained cross-lingual models often underperform on low-resource languages. We propose Multi-lingual language model Fine-Tuning (MultiFiT) to enable practitioners to train and fine-tune language models efficiently in their own language. In addition, we propose a zero-shot method using an existing pretrained cross-lingual model. We evaluate our methods on two widely used cross-lingual classification datasets where they outperform models pretrained on orders of magnitude more data and compute. We release all models and code.