Posts about different aspects of transfer learning.
natural language processing
Over the last years, models in NLP have become much more powerful, driven by advances in transfer learning. A consequence of this drastic increase in performance is that existing benchmarks have been left behind. Recent models "have outpaced the benchmarks to test for them" (AI Index Report 2021)
language models
This article provides an overview of recent methods to fine-tune large pre-trained language models.
transfer learning
This post summarizes progress in 10 exciting and impactful directions in ML and NLP in 2020.
natural language processing
This post gathers ten ML and NLP research directions that I found exciting and impactful in 2019.
cross-lingual
This post expands on the ACL 2019 tutorial on Unsupervised Cross-lingual Representation Learning. It highlights key insights and takeaways and provides updates based on recent work, particularly unsupervised deep multilingual models.
transfer learning
This post expands on the NAACL 2019 tutorial on Transfer Learning in NLP. It highlights key insights and takeaways and provides updates based on recent work.
events
This post discusses highlights of NAACL 2019. It covers transfer learning, common sense reasoning, natural language generation, bias, non-English languages, and diversity and inclusion.
transfer learning
This post discusses my PhD thesis Neural Transfer Learning for Natural Language Processing and some new material presented in it.
events
This post discusses highlights of AAAI 2019. It covers dialogue, reproducibility, question answering, the Oxford style debate, invited talks, and a diverse set of research papers.
transfer learning
This post gathers 10 ideas that I found exciting and impactful this year—and that we'll likely see more of in the future. For each idea, it highlights 1-2 papers that execute them well.
events
This post discusses highlights of EMNLP 2018. It focuses on talks and papers dealing with inductive bias, cross-lingual learning, word embeddings, latent variable models, language models, and datasets.
language models
This post expands on the Frontiers of Natural Language Processing session organized at the Deep Learning Indaba 2018. It discusses major recent advances in NLP focusing on neural network-based methods.