CIS 607: Seminar on Deep Learning for Natural Language Processing
Course Description
Deep learning has recently proved itself as a powerful branch of machine learning that has applications in a variety of domains (i.e., computer vision, robotics, etc.). In natural language processing (NLP), deep learning has been transformative and led to a new generation of methods with better performance, portability and robustness (e.g, the machine translation systems). All of those advances are very recent and the demand for data scientists with deep learning expertise is growing very quickly. At the beginning of this seminar, we will cover the basic concepts of deep learning and NLP, and possibly provide some hand-on experience to implement the models. Afterward, we will review and discuss a collection of research papers on NLP with deep learning, including but not limited to the typical tasks of question answering, information extraction, natural language inference, dialog and summarization.Prerequisites: Some background in algebra and machine learning (i.e., CIS 472/572). Please talk with the instructor if you are not sure if you have the necessary background.
Class Location: 200 Deschutes Hall
Class Time: Friday, 14:30 pm to 15:50 pm Instructor: Thien Huu Nguyen
Email: thien AT cs.uoregon.edu
Office: Room 330, Deschutes Hall
Course Material:
Goldberg. Neural Network Methods for Natural Language Processing 2017. (free download when on UO campus). Kyunghyun Cho's note ACL Anthology (free and covering the papers in most of the major NLP venues) Class Organization The grading for this class is P/NP. Each student in the class will need to choose some paper on some particular topic, present them in one of the classes and lead the discussion on the topic. Each presentation will be given 30 minutes along with 5-10 minutes for discussions. More discussion is encouraged on Piazza. After the presentation, the presenter needs to submit a summary about the presented paper/topic. The summary should follow the NAACL format (a.k.a. ACL style for 8.5x11 paper). The required length of the summary is between 2 and 3 pages. For each presentation, we will have one student (other than the presenter) to serve as the reviewer. The role of the reviewer is to provide judgement/comments/suggestion or ask questions about the paper/topic in the discussion time after the presentation. Although all students need to read and understand the papers being presented before each class to be able to actively contribute to the discussions, the reviewer would help to provide deeper judgement by reading and thinking critically about the papers/topics ahead of time.