Course Description

CS 11-711
Language Technologies Institute, School of Computer Science
Carnegie Mellon University
Tuesday/Thursday 12:30-1:50pm, Tepper 1403

Instructors/TAs:

(See Piazza for Office Hours)

Instructors:
Daniel Fried (dfried@cs.cmu.edu)
Robert Frederking (ref@cs.cmu.edu)

TAs: (anlp-fall-2023@mailman.srv.cs.cmu.edu)
  Aprameya Bharadwaj
  Atharva Kulkarni
  Bowen Tan
  Robert (Chi-Fan) Lo
  Saujas Vaduguru
  Sang Choe
  Sophia (Yi-Hui) Chou
  Zora (Zhiruo) Wang
Questions and Discussion: Ideally in class or through piazza so we can share information with the class, but emailing the TA mailing list and coming to office hours are also encouraged.

Course Description

Advanced natural language processing is an introductory graduate-level course on natural language processing aimed at students who are interested in doing cutting-edge research in the field. In it, we describe fundamental tasks in natural language processing such as syntactic, semantic, and discourse analysis, as well as methods to solve these tasks. The course focuses on modern methods using neural networks, and covers the basic modeling and learning algorithms required therefore. The class culminates in a project in which students attempt to reimplement and improve upon a research paper in a topic of their choosing.

Pre-requisites: There are no hard pre-requisites for the course, but programming experience in Python and knowledge of probability and linear algebra are expected. It will be helpful if you have used neural networks previously.

Class format: For each class there will be:

  • Reading: Most classes will have associated reading material that we recommend you read before the class to familiarize yourself with the topic.
  • Lecture and Discussion: There will be a lecture and discussion regarding the class material. This will be recorded and posted online for those who cannot make the in-person class.
  • Code/Data Walk: Some classes will involve looking through code or data.
  • Quiz: There will be a quiz covering the reading material and/or lecture material that you can fill out on Canvas for one or two days after the class.

Grading: The assignments will be given a grade of A+ (100), A (96), A- (92), B+ (88), B (85), B- (82), or below. The final grades will be determined based on the weighted average of the quizzes, assignments, and project. Cutoffs for final grades will be approximately 97+ A+, 93+ A, 90+ A-, 87+ B+, 83+ B, 80+ B-, etc., although we reserve some flexibility to change these thresholds slightly.

  • Quizzes: Worth 20% of the grade. Your lowest 3 quiz grades will be dropped.
  • Assignments: There will be 4 assignments (the final one being the project), worth respectively 15%, 15%, 20%, 30% of the grade.
The details of the assignments are elaborated on the assignments page.

Class Format:

We encourage you to attend in-person to make it easier to ask and answer questions, but will also stream on Zoom and record. If you are waitlisted, please attend on Zoom for now as the room has limited capacity.