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 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!

We are hiring new faculty! See here the job advertisement.


Michael Hahn and I are excited to share the following paper, which has just been accepted to appear in Cognition. Modeling Task Effects in Human Reading with Neural Network-based Attention

[1/5] Without ASR/MT pretraining, E2E ST performs poorly; Really?

In our #ICML2022 paper, we revisited E2E ST from scratch trained on speech-translation pairs alone with adapted techs, which yields comparable results to previous work using pretraining. @bazril @RicoSennrich

ACL week has started and I'm happy as a clam!🤩If idioms fascinate you too: stop by poster session 1 tomorrow at 11AM (interpretability session), where I will present work with @iatitov and Chris Lucas on idioms in NMT! Some highlights... 1/9

Congratulations to Dr. @BZhangGo , who just successfully defended his PhD "Towards Efficient Universal Neural Machine Translation"!

With thanks to the examiners, @zngu and @gneubig!

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