We are a collocation of collaborators working on a diverse range of topics in computational linguistics, natural language processing and machine learning.

Lab Motto: We put the fun in funicular!

Current Foci

  • Decoding Strategies for Neural MT
  • Information-Theoretic Linguistics
  • Computational Typology
  • Computational Morphology
  • Bias and Fairness in NLP Systems
  • Computational Approaches to Metaphor
  • Low-resource Linguistic Annotation
  • Algorithms for Parsing
  • Interpreting Neural Representations of Language
  • Computational Social Science
  • NLP Applications

Lab News



Natural Language Processing

ETH Zürich Fall 2023
This course introduces modern techniques in NLP, primarily focusing on statistical and deep learning, emphasizing algorithmic aspects of structured models. We provide an overview of the primary areas of research in language processing as well as a detailed exploration of the models and techniques used both in research and in commercial natural language systems. Topics include backpropagation, sentiment analysis, language modeling, part-of-speech tagging, (semantic) parsing, machine translation, and the bias and fairness of NLP models.

Generating Text from Language Models

ACL (Toronto) July 2023
In this tutorial, we will provide a centralized and cohesive discussion of critical considerations when choosing how to generate text from a language model. We will cover a wide range of empirically-observed problems (like degradation, hallucination, repetition) and their corresponding proposed algorithmic solutions from recent research (like top-p sampling and its successors). We will then cover methods in controlled generation, that go beyond just ensuring coherence to ensure text exhibits specific desired properties.

Advanced Formal Language Theory

ETH Zürich Spring 2023
This course serves as an introduction to various advanced topics in formal language theory. The primary focus of the course is on weighted formalisms, which can easily be applied in machine learning. Topics include finite-state machines as well as the algorithms that are commonly used for their manipulation. We will also cover weighted context-free grammars, weighted tree automata, and weighted mildly context-sensitive formalisms.

Large Language Models

ETH Zürich Spring 2023
Large language models have become one of the most commonly deployed NLP inventions. In the past half-decade, their integration into core natural language processing tools has dramatically increased the performance of such tools, and they have entered the public discourse surrounding artificial intelligence. In this course, we start with the probabilistic foundations of language models, i.e., covering what constitutes a language model from a formal, theoretical perspective. We then discuss how to construct and curate training corpora, and introduce many of the neural-network architectures often used to instantiate language models at scale. The course covers aspects of systems programming, discussion of privacy and harms, as well as applications of language models in NLP and beyond.

Philosophy of Language and Computation II

ETH Zürich Spring 2023
This graduate class, taught like a seminar, is designed to help you understand the philosophical underpinnings of modern work in natural language processing (NLP), most of which centered around statistical machine learning applied to natural language data.

ESSLLI 2023 Tutorial: Formal Language Theory and Neural Networks

ETH Zürich Spring 2023

Thesis Projects

If you are a MSc student at ETH Zurich interested in writing your thesis with us, we would be delighted to hear from you! Unfortunately, we do not have the capacity to consider students from outside ETH for thesis projects. Our research revolves around theoretical and applied problems in Natural Language Processing, Computational Linguistics, Machine Learning and Statistics. To obtain a better understanding of what currently interests us, we invite you to check our recent publications. However, feel free to express interest in any topic you think our group might be well suited to advise you on: Just because we have not yet looked into a topic does not mean we are not interested in it or willing to become interested in the topic.
Please send an email to ryan.cotterell@inf.ethz.ch with CC to afra.amini@inf.ethz.ch, clara.meister@inf.ethz.ch, and niklas.stoehr@inf.ethz.ch. State either [bachelor’s thesis] or [master’s thesis] at the start of the subject. For us to get to know you a little, please write a paragraph introducing yourself and why you are interested in working with us. It would help us a lot if you also provided a list of four or five more concrete topics that you are interested in. We will try our best to find a project that suits your interests. We are looking forward to receiving your inquiry!

Joining Our Lab

Thank you very much for your interest in joining our group – unfortunately, we are not accepting PhD students anymore!

If you are interested in working with us as a Master’s student, please see here. Ryan has previously co-advised Master’s students on NLP topics with Mrinmaya Sachan and others, if co-advising is an option you would like to pursue. At Cambridge, Ryan co-advises MPhil students with Simone Teufel. We are looking forward to receiving your inquiry!