Computational Semantics for Natural Language Processing

ETH Zürich, Spring Semester 2024: Course catalog

Course Description

This course presents an introduction to Natural language processing (NLP) with an emphasis on computational semantics i.e. the process of constructing and reasoning with meaning representations of natural language text.

The objective of the course is to learn about various topics in computational semantics and its importance in natural language processing methodology and research. Exercises and the project will be key parts of the course so the students will be able to gain hands-on experience with state-of-the-art techniques in the field.


The final assessment will be a combination of a group paper presentation (10%), two graded exercises (40%) and the project (50%). There will be no written exams.

Lectures: Fri 14:00-16:00 (CAB G61)

Discussion Sections: Fri 16:00-17:00

Office Hour (assignment, project): Please contact professor/TAs for appointment.

Textbooks: We will not follow any particular textbook. We will draw material from a number of research papers and classes taught around the world. However, the following textbooks would be useful:

  1. Introduction to Natural Language Processing by Jacob Eisenstein
  2. Speech and Language Processing by Jurafsky and Martin


15.02   Class website is online!

Course Schedule

 Lecture Date Description Course Materials Events            Exercise TA
  1  23.02     Introduction Diagnostic Quiz
Answers to quiz
Guidelines for Paper Presentation
Presentation preference indication  
  2  01.03  The Distributional Hypothesis and Word Vectors 1. Glove    
 Voluntary  01.03  PyTorch: Matrix Calculus and Backpropagation 1. CS231n notes on network architectures
2. CS231n notes on backprop
3. Learning Representations by Backpropagating Errors
4. Derivatives, Backpropagation, and Vectorization
5. Yes you should understand backprop
  3  08.03   Word Vectors 2, Word Senses and Sentence Vectors

(Recursive and Recurrent Neural Networks)
1. Unsupervised Word Sense Disambiguation Rivaling Supervised Methods
2. Improving Vector Space Word Representations Using Multilingual Correlation
3. Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
 Voluntary  08.03   Final projects (Introduction and Guidelines) 1. Guidelines
2. Suggested projects
 4  15.03  NLU beyond a sentence

Seq2Seq and Attention

Case Study: Sentence Similarity, Textual Entailment and Machine Comprehension
1. Massive Exploration of Neural Machine Translation Architectures
2. Bidirectional Attention Flow for Machine Comprehension
 Voluntary  15.03  Project rotation:
bring your project title;
find your supervised TAs
    All TAs
 5  22.03  Syntax and Predicate Argument Structures

(Semantic Role Labelling, Frame Semantics, etc.)
1. Stanford’s Graph-based Neural Dependency Parser at the CoNLL 2017 Shared Task
2. Grammar as a foreign language
Assignment 1 released  
 Voluntary  22.03  Project proposal discussion 1 Project feasiblity, topic, and proposal summary
bring ideas (possibly even slides if needed) for discussion
  Mrinmaya, Shehzaad, Yifan, Sankalan (maybe)
 Easter  29.03         
 Easter  05.04         
 6  12.04  Predicate Argument Structures II

(Semantic Role Labelling, Frame Semantics, etc.)
1.Jointly Predicting Predicates and Arguments in Neural Semantic Role Labeling
2.Frame-Semantic Parsing
Project proposal due  
 Voluntary  12.04  Project proposal discussion 2 Project feasiblity, topic, and proposal summary   All TAs
 7  19.04  Modelling and tracking entities: NER, coreference and information extraction (entity and relation extraction) 1. End-to-end Neural Coreference Resolution
2. Improving Coreference Resolution by Learning Entity-Level Distributed Representations
 Voluntary  19.04  Cluster usage (Guidelines)     Shehzaad
 8  26.04  Formal Representations of Language Meaning 1.Compositional semantic parsing on semi-structured tables
2.Supertagging With LSTMs
Assignment 1 due (28.04)
Assignment 2 release (28.04)
Project proposal grade out
 Voluntary  26.04  Assignment 1 QA
Project QA (optional)
    All TAs
 9  03.05  Transformers and Contextual Word Representations (BERT, etc.)
1. Big Bird: Transformers for Longer Sequences (Only cover the idea of sparse attention: don’t need to cover turing completeness and the theoretical results))
2. BERT rediscovers the classical NLP pipeline
 Voluntary  03.05  Optional schedule meeting with TA if necesary    
 10  10.05  Natural Language Generation

Case Study: Summarization and Conversation Modelling
1. Language Models are Unsupervised Multitask Learners
2. BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension
 Voluntary  10.05  Huggingface and Transformers
1. Huggingface
 11  17.05  Question Answering
1. Reading Wikipedia to Answer Open-Domain Questions
2. Latent Retrieval for Weakly Supervised Open Domain Question Answering
Assignment 1 grade out  
 Voluntary  17.05  Project progress discussion 1 Project progress, problems, whole storyline
Schedule (TBD)
  All TAs
 12  24.05  Pragmatics 1. Pragmatic Language Interpretation as Probabilistic Inference
2. Rational speech act models of pragmatic reasoning in reference games
Project mid-term report due  
 Voluntary  24.05  Optional     All TAs
 13  31.05  Language + {Knowledge, Vision, Action} 1. Knowledge Enhanced Contextual Word Representations
2. VisualBERT: A Simple and Performant Baseline for Vision and Language
Assignment 2 due (01.06)  
 Voluntary  31.05  Optional     All TAs
   21.06      Assignment 2 grade out
Project report due
   12.07    Schedule, and link of the GatherTown Poster session  

Assignment Submission Instructions




You can ask questions on moodle. Please post questions there, so others can see them and share in the discussion. If you have questions which are not of general interest, please don’t hesitate to contact us directly.

Lecturer Mrinmaya Sachan
Guest Lecturers Avinava DubeyAlex WarstadtEthan Wilcox
Teaching Assistants Shehzaad DhuliawalaYifan HouSankalan Pal Chowdhury,  Tianyang Xu