CSCI 544: Applied NLP

Annoucements

Check Brightspace. 

[08/13] I don't know when the grader positions for this class will be available. 

Summary

This course covers both fundamental and cutting-edge topics in Natural Language Processing (NLP) with a focus on Language Models. Natural language processing (NLP) has been revolutionized by the advancement of large-scale language models, achieving state-of-the-art performance across a wide variety of tasks. This course will cover the fundamentals of language modeling and related topics in natural language processing, deep learning, and machine learning. Students will gain familiarity with the capabilities of large language models as well as get hands-on experience with building and evaluating small-scale language models. The class will also explore the real-world consequences of deploying language models, such as the ethics and harms associated with them.

Calendar + Syllabus

[Tentatively for now]

Calendar and prespecified syllabus are subject to change. More details, e.g., reading materials and additional resources, will be added as the semester continues. All work (except the project final report) is due on the specified date by 11:59 pm PT.

Assignments and Grading

There will be three components to course grades:

• Homeworks (20%).

  • 5% X 4: There will be four coding homework assignments based on the topics of the class.

• Quizzes (10%).

  • 2% X 5: Multiple-Choice Questions and Short Answers. Missed quizzes will receive a zero grade, and there will be no make-up quizzes.

• Class Projects (45%).

  • Each student will do a group class project based on the topics covered in the class. Students will propose their own project, do the research and build a proof-of-concept, create a video demonstration of the proof-of-concept, and present the project in their report.

  • Proposal: 5%

  • Status Reports: 10%

  • Project Presentation: 10%

  • Final Write-up: 20%

• Exams (25%)

  • Midterm (10%): The midterm exam will contain a mixture of multiple-choice and long-form questions, covering about the first half of the material covered in the class.

  • Final (15%): The final exam at the end of the semester, covering all of the material covered in the class, will contain a mixture of multiple-choice and long-form questions.

Grading inquiries and questions about the grading of the homework and the quizzes can be asked (to the TAs) within two weeks from the grading date (the date the grades are released). Grades will be available within 2-2.5 weeks after submission.

All written assignments related to the final project should use the standard *ACL paper submission template.

Late Days

Students are allowed a maximum of 6 late days total for all assignments (but NOT the quiz sheets). You may use up to 3 late days per assignment. Using one late day for a project assignment involves each of the teammates using a late day each. Partial late days are not permitted. For every extra late day beyond the allowed late days, the student / team will lose 20% of the grade for the assignment.

Note: Please familiarize yourself with the academic policies and read the note about student well-being.

Reference Texts

The following texts are useful, but none are required. All of them can be read free online.

• Dan Jurafsky and James H. Martin. Speech and Language Processing (2024 pre-release)

• Jacob Eisenstein. Natural Language Processing

• Yoav Goldberg. A Primer on Neural Network Models for Natural Language Processing

• Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Deep Learning

• Delip Rao and Brian McMahan. Natural Language Processing with PyTorch (requires Stanford login).

• Lewis Tunstall, Leandro von Werra, and Thomas Wolf. Natural Language Processing with Transformers

If you have no background in neural networks but would like to take the course anyway, you might well find one of these books helpful to give you more background:

• Michael A. Nielsen. Neural Networks and Deep Learning

• Eugene Charniak. Introduction to Deep Learning