Natural Language Processing Learning to Reason Over Tables from Less Data In "Understanding tables with intermediate pre-training", published in Findings of EMNLP 2020, we introduce the first pre-training tasks customized for table parsing, enabling models to learn better, faster and from less data.
Natural Language Processing Efficient multi-lingual language model fine-tuning Our latest paper studies multilingual text classification and introduces MultiFiT, a novel method based on ULMFiT. MultiFiT, trained on 100 labeled documents in the target language, outperforms multi-lingual BERT, and the LASER algorithm—even though LASER requires a corpus of parallel texts.
Natural Language Processing Text Classification with TensorFlow Estimators Throughout this post we will explain how to classify text using Estimators, Datasets and Feature Columns, with a scalable high-level API in TensorFlow.
