Natural Language Processing

The course provides an extensive and in-depth introduction to the state of the art and the main research trends in Natural Language Processing (NLP). In particular, the course focus on deep learning methods for NLP, with a specific attention on large language models. Students will deal with fundamental tasks such as syntactic, semantic, and discourse analysis, as well as methods to solve these tasks. A specific focus will be on transfer learning methods and model architectures to address concrete tasks such as text classification, question answering, automatic translation and text generation. These goals will be pursued by a combination of theory, seminars on recent papers and methods, and practical examples. The program is intended for graduate students in computer science and data science who are familiar with machine learning basics. An intruduction to deep learning and neural networks will be provided together with a practical introduction to PyTorch. Coding in Python will play also an important role in the classes.

Through reading recent research papers, programming assignments, and a final project, students will acquire the following skills: 1) knowing and understanding the main topics as well as the research issues and the future trends in the field of Natural Language Processing (NLP); 2) learn how to apply NLP methods to a corpus of texts for a specific need; 3) being able to judge the quality of different design and implementation choices when coming to a NLP project; 4) being able to design, implement, and evaluate a specific project focused on NLP tasks; 5) understand the notion of language model and being able to detect language specificities and topics in a corpus of text documents; 6) being able to use the Python stack of libraries and tools required to develop a NLP project.