Logistics

Time/Location:

• Tuesdays 1:30pm-2:50pm in CS 301
• Office hours:
  Karthik Narasimhan: by appointment
  Xi Chen: Fridays 1:30 - 3:30pm, CS 003

How to contact us: Please use Piazza for all questions related to the IW seminar and to find announcements. For external queries, emergencies, or personal matters, you can use a private Piazza post visible only to Instructors.

Content

What is this seminar about?

Recent advances in deep learning have led to exciting developments in natural language processing, especially in areas like translation, question answering and information extraction. This seminar will allow students to choose and work on a research project utilizing deep neural networks for NLP. The project can focus on either new theoretical/algorithmic developments for NNs, applying neural network models to solve various NLP tasks, or development of new evaluation frameworks, tasks and metrics.
There are no prerequisites for this seminar beyond COS 217, COS 226 and one of COS 324 (Machine Learning), 484 (NLP), 485 (Neural Networks) or a similar machine learning course. Prior experience with NLP and Python programming will be useful. Students may work in pairs IF the project can be split so that each student has their own semester-size piece of the project. We will spend the first 1-2 meetings of the semester surveying ideas in the field, brainstorming ideas and developing project proposals. The remaining meetings will be used for project updates, formal student presentations, and discussions on how to perform background research, prepare a presentation and write a final paper.

Resources

Courses:

• COS 484, Princeton: Fall 2019
• CS 224n: NLP with Deep Learning taught by Chris Manning, Stanford

Readings:

Some helpful books to read up on background material:

• Yoav Goldberg. A Primer on Neural Network Models for Natural Language Processing.
• Dan Jurafsky and James H. Martin. Speech and Language Processing (3rd ed. draft).
• Ian Goodfellow and Yoshua Bengio and Aaron Courville Deep Learning
• Jacob Eisenstein. Natural Language Processing

Useful web resources:

• Introduction to PyTorch
• Visualizing seq2seq and attention

Funding:

• You may apply for funding to support your IW project (deadline in Feb, exact date will be announced soon on the general IW page)

Schedule

Pre-class action items should be completed by 11:59pm on Mondays before class on Tuesdays. Please also keep track of general IW events and deadlines here.

Date	Topic	Pre-class Action items
Feb 4	Introduction	Fill out this form
Feb 6	Information meeting for all IW students (CS 105, 4:30-5:30pm)
Feb 7	Introduction to PyTorch
Feb 11	Brainstorming project ideas	Project ideas form
Feb 18	Project proposal presentations	Presentation in Google slides
Feb 25	Project updates	Update Google slides
Mar 3	Project updates	Update Google slides
Mar 8	Submit checkpoint form
Mar 10	Project updates	Update Google slides
Mar 17	No class (Spring recess)
Mar 24	Project updates	Update Google slides
Mar 31	Project updates, Attend "How to Give an IW talk" in CS 105 at 4:30pm	Update Google slides
Apr 7	Project updates	Update Google slides
Apr 14	Project updates, Attend "How to write an IW paper" in CS 105 at 4:30pm	Update Google slides
Apr 26	Submit slides for Oral Presentation
Apr 28	In-class oral presentations
May 12	Written final report due