HIOK0010EU Representation Learning for Natural Language Processing (RL4NLP)

Volume 2024/2025

Representation Learning for Natural Language Processing (RL4NLP) is a course that focuses on studying techniques for representing language units such as words, sentences, documents, and knowledge in a numerical format. The course elaborates on methods for training such representations, interpreting them, and their application in downstream tasks.

Learning Outcome

This course will give the students:

Knowledge and understanding of:

• theories and methods of relevance to representation learning for natural language elements such as words, sentences, documents, and knowledge when expressed through natural language

• problems related to embeddings of natural language elements into a numerical feature space and unveiling the implicit learning within deep learning methods for natural language processing.

Skills in

• discussing and documenting problems of relevance to language embeddings and representation learning

• proposing and evaluating solutions for relevant problems in representation learning

Competencies in

• describing and analysing advanced topics within representation learning through deep neural networks for language

• designing and documenting relevant solutions.

MA-level
2019 curriculum

See all the curriculums.

Teaching and learning methods

Our teaching methodology will combine traditional lectures with interactive exercise classes and hands-on project work. We provide individual supervision for projects and assignments and offer students the opportunity to engage with current scientific literature by presenting papers during the course. As we evolve, we aim to transition towards a flipped classroom model to enhance the student's learning journey further.

Lectures: Engaging lectures and interactive sessions will provide comprehensive coverage of fundamental concepts and advanced topics in representation learning. They will also encourage participation and facilitate deeper understanding through discussions and Q&A sessions.
Project Work: Hands-on projects will allow students to apply their learning to real-world scenarios and tacke possible challenges in their study. They will be supported by individual supervision to ensure successful project completion.
Scientific Paper Presentations: Students will present scientific papers relevant to the course material. This activity will enhance research skills, presentation abilities, and critical analysis of scientific literature.
Transition to Flipped Classroom: In future course iterations, we will gradually implement the flipped classroom teaching method. Pre-recorded lectures and online resources will be provided before in-class sessions, allowing for more interactive and applied learning during class time. This approach enables the students to take ownership of their learning, fosters deeper engagement, and encourages active participation.

Workload