The Natural Language Processing Group at the University of Edinburgh (EdinburghNLP) is a group of faculty, postdocs, and PhD students working on algorithms that make it possible for computers to understand and produce human language. We do research in all core areas of natural language processing, including morphology, parsing, semantics, discourse, language generation, and machine translation. EdinburghNLP also has a strong track record of work at the interface of NLP with other areas, including speech technology, machine learning, computer vision, cognitive modeling, social media, information retrieval, robotics, bioinformatics, and educational technology.

With 11 core faculty members, EdinburghNLP is one of the largest NLP group in the world. It is also ranked as the most productive group in the area, according to csrankings.org. Our achievements include the award-winning neural machine translation system Nematus and the high-performance language modeling toolkit KenLM. EdinbughNLP faculty have a strong record of getting high-profile grants, and have so far won a total of five European Research Council (ERC) grants.

We are looking for new PhD students! Join us. Also, please check out the new UKRI Centre for Doctoral Training in Natural Language Processing!

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@wzuidema @clark_kev @ukhndlwl @omerlevy @chrmanning @ACL2019_Italy There are interesting similarities to what we found for NMT Transformers (Lena Voita's ACL paper: https://t.co/965F5MKnMR): many heads captured syntax and these heads were the most important ones. It would interesting to contrast Transformer head funcs with different objectives.

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Dear ICML fellas, tomorrow I'll present my recent work Question Answering by Reasoning Across Documents with Graph Convolutional Networks (https://t.co/ea79FWHfa5) at the Learning and Reasoning with Graph-Structured Representations. Come and check it out!

Dear ICML fellas, tomorrow I'll present my recent work Block Neural Autoregressive Flow (https://t.co/sZZcWOCD2i) at the Workshop on Invertible Neural Nets and Normalizing Flows. Come and check it out! Code available here: https://t.co/5EJobraAmB

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