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!

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

news

🚨 Job Alert 🚨
Faculty position @EdinburghUni

@InfAtEd is hiring a "Permanent" Assistant/Associate Professor in Computational Social Science to join @SMASH_Edin & work closely with @EdinburghNLP & @uoessps

Deadline: 25 Apr 2023

Share the word 📢📢

https://elxw.fa.em3.oraclecloud.com/hcmUI/CandidateExperience/en/sites/CX_1001/job/6904

Introducing *nanoT5*

Inspired by @jonasgeiping's Cramming and @karpathy's nanoGPT, we fill the gap of a repository for pre-training T5-style "LLMs" under a limited budget (1xA100 GPU, ~20 hours) in PyTorch

🧑‍💻http://github.com/PiotrNawrot/nanoT5

@EdinburghNLP

In our new survey “Modular Deep Learning”, we provide a unified taxonomy of the building blocks of modular neural nets and connect disparate threads of research.

📄 https://arxiv.org/abs/2302.11529
📢 http://www.ruder.io/modular-deep-learning/
🌐 http://www.modulardeeplearning.com
w/ @PfeiffJo @licwu @PontiEdoardo

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