This post is an interview by fast.ai fellow Sanyam Bhutani with me. This post originally appeared at HackerNoon with a different introduction. I had the honour to be interviewed by Sanyam Bhutani, a Deep Learning and Computer Vision practitioner and fast.ai fellow who’s been doing a series interviewing people that inspire him. To be honest, it feels surreal to be the one being interviewed. I hope my answers may be interesting or useful to some of you. Sanyam: Hello Sebastian, […]

The post discusses highlights of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP 2018). This post originally appeared at the AYLIEN blog. You can find past highlights of conferences here. You can find all 549 accepted papers in the EMNLP proceedings. In this review, I will focus on papers that relate to the following topics: Inductive bias The inductive bias of a machine learning algorithm is the set of assumptions that the model makes in order to […]

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, I will highlight 1-2 papers that execute them well. I tried to keep the list succinct, so apologies if I did not cover all relevant work. The list is necessarily subjective and covers ideas mainly related to transfer learning and generalization. Most of these (with some exceptions) are not trends (but I suspect that some […]

This post discusses 4 major open problems in NLP based on an expert survey and a panel discussion at the Deep Learning Indaba. This is the second blog post in a two-part series. The series expands on the Frontiers of Natural Language Processing session organized by Herman Kamper, Stephan Gouws, and me at the Deep Learning Indaba 2018. Slides of the entire session can be found here. The first post discussed major recent advances in NLP focusing on neural network-based […]