The University of Texas at Austin
Recent progress in machine learning, especially in deep learning, has led to encouraging advances in Natural Language Processing: state-of-the-art results on benchmark datasets are getting renewed at a rapid pace. However, most of these methods rely on a large amount of labeled training data. Yet, high quality labeled textual data is expensive and difficult to build; the amount of labeled data is destined to be small compared to the vastness and dynamics of language (i.e., the number of different languages, possible genres/domains, and language evolution). In this seminar, we discuss several ways to address this bottleneck, including semi-supervised learning, noisy supervision, transfer learning, active learning, and domain adaptation. We focus on the unique challenges of natural language understanding and generation tasks.
Graduate standing. Prior coursework in Computational Linguistics/Natural Language Processing/Machine Learning/a related field in AI, or instructor consent.
There will be no textbook for this seminar; reading material will consists of technical papers discussed in each meeting.
If you turn in your assignment late, expect points to be deducted. Extensions will be considered on a case-by-case basis. If you anticipate that you will need an extension for some assignment, let me know in advance.
By default, 5 points (out of 100) will be deducted for lateness, plus an additional 1 point for every 24-hour period beyond 2 that the assignment is late. The maximum extension penalty is 40 points if handed in before the last day of class. Resubmissions of assignments are allowed; extension penalty applies for post-deadline resubmissions. Note that there are always some points to be had, even if you turn in your assignment late. So if you would like to know if you should still turn in the assignment even though it is late, the answer is always yes.
You are encouraged to discuss your project with classmates. All written work must be your own. Students caught cheating will automatically fail the course. If in doubt, ask the instructor.
Note that this is a preliminary schedule that is subject to change.