5 citations · 5 across the 1 of their papers we have counts for
4 papers
AxCell: Automatic Extraction of Results from Machine Learning Papers
Marcin Kardas, Piotr Czapla, Pontus Stenetorp +4
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 le…
MultiFiT: Efficient Multi-lingual Language Model Fine-tuning
Julian Martin Eisenschlos, Sebastian Ruder, Piotr Czapla +3
Pretrained language models are promising particularly for low-resource languages as they only require unlabelled data. However, training existing models requires huge amounts of co…
Applying a Pre-trained Language Model to Spanish Twitter Humor Prediction
Bobak Farzin, Piotr Czapla, Jeremy Howard
Our entry into the HAHA 2019 Challenge placed in the classification task and in the regression task. We describe our system and innovations, as well as comparing…
Universal Language Model Fine-Tuning with Subword Tokenization for Polish
Piotr Czapla, Jeremy Howard, Marcin Kardas
Universal Language Model for Fine-tuning [arXiv:1801.06146] (ULMFiT) is one of the first NLP methods for efficient inductive transfer learning. Unsupervised pretraining results in…