uva-nlp-course

CS 4501 Machine Learning for NLP

• Instructor: Yangfeng Ji
• Semester: Fall 2020
• Location: Online
• Time: Monday and Wednesday 3:30 PM - 4:45 PM
• TA: Stephanie Schoch
• Office Hours:
  • Yangfeng Ji: Monday 5 - 6 PM
  • Stephanie Schoch: Tuesday 9 - 9:30 AM and Wednesday 2 - 3 PM
  • Location: Zoom

Note:

• Nov. 10, 2020: This webpage is currently for CS 4501. For CS 6501 in Spring 2021, the topics will be similar, but the lecture content and homework assignment will be different.

1. Highlights

We will use Zoom for our online teaching. Students can find the Zoom links on Collab, under the Online Meetings tab. We will also record our lectures and upload to Collab.

• Online teaching information
• Schedule
• Piazza
• Homework submission template for homework assignments
• All course lectures will be recorded and uploaded to Collab. Please do not distribute the videos outside the class. Please refer to this page for more information about the recording policy.

Final Project Related

• Final Project Signup Form
• Final Project Proposal Template
  • Uncomment the line “\aclfinalcopy”
  • Uncomment the line “\def\aclpaperid{***}” and replace “***” with the team No.
• Final Project Rubric

2. Course Description

Natural language processing (NLP) seeks to provide computers with the ability to process and understand human language intelligently. Examples of NLP techniques include (i) automatically translating from one natural language to another, (ii) analyzing documents to answer related questions or make related predictions, and (iii) generating texts to help story writing or build conversational agents. This course, consisting of one fundamental part and one advanced part, will give an overview of modern NLP techniques.

2.1 Topics

This course will mainly focus on applying machine learning (particularly, deep learning) techniques to natural language processing. NLP topics covered by this course

1. Text classification
2. Language modeling
3. Word embeddings
4. Sequence labeling
5. Machine translation
6. Some advanced topics: discourse processing, conference resolution, text generation, and interpretability in NLP

For detail information, please refer to the course schedule.

2.2 Prerequisites

• Proficiency in Python
  This course requires some programming in both homeworks and the final project. The preference of programming language for this course is Python (with some additional packages like Scipy, Sklearn, and PyTorch).
• Calculus and Linear Algebra
  Multivariable derivatives, matrix/vector notations and operations; singular value decomposition, etc.
• Probability and Statistics
  Mean and variance, multinomial distribution, conditional dependence, maximum likelihood estimation, Bayes theorem, etc.
• Foundations of Machine Learning
  Logistic regression, cross validation, optimization with gradient descent, bias and variance decomposition, etc.

2.3 Textbooks

• [JE] Eisenstein, Natural Language Processing, 2018

Supplemental materials

• Jurafsky and Martin, Speech and Language Processing, 3rd Edition, 2019
• Smith, Linguistic Structure Prediction, 2009
• Shalev-Shwartz and Ben-David, Understanding Machine Learning: From Theory to Algorithms, 2014
• Goodfellow, Bengio and Courville, Deep Learning, 2016

3. Assignments and Final Project

• Homework (84%):
  • There will be six homeworks and each of them is worth 14%.
  • Students are allowed to discuss homework with their classmates. But, directly copying answers from others is definitely considered as plagiarism.
• Project (16%):
  There is only one course project and the credit breaks down to four parts
  • Project proposal: 4%
  • Final project presentation: 6%
  • Final project report: 6%
  • Other than using the machine learning libraries including Sklearn, PyTorch, Tensorflow, students need to implemented the rest of the proposed model by themselves. Copying code from any resources (e.g., Github, Bitbucket, and Gitlab) is prohibited and will be considered as plagiarism.
  • Students should team up for this project, each group should have 2 - 3 students.

4. Grading Policy

• General information

5. Other Related Statements

5.1 Honor code

We are pround of our honor system and I trust every student in this course to fully comply with all of the provisions of the University’s Honor Code. By enrolling in this course, you have agreed to abide by and uphold the Honor System of the University of Virginia, as well as the following policies specific to this course.

• All graded assignments must be pledged.
• All suspected violations will be forwarded to the Honor Committee, and you may, at my discretion, receive an immediate zero on that assignment regardless of any action taken by the Honor Committee.

Please let me know if you have any questions regarding the course Honor policy. If you believe you may have committed an Honor Offense, you may wish to file a Conscientious Retraction by calling the Honor Offices at (434) 924-7602. For your retraction to be considered valid, it must, among other things, be filed with the Honor Committee before you are aware that the act in question has come under suspicion by anyone. More information can be found at http://honor.virginia.edu. Your Honor representatives can be found at: http://honor.virginia.edu/representatives.

Adapted from Honor Syllabus Example Statement on the UVa Honor Committee website

5.2 Students with disabilities or learning needs

It is my goal to create a learning experience that is as accessible as possible. If you anticipate any issues related to the format, materials, or requirements of this course, please meet with me outside of class so we can explore potential options. Students with disabilities may also wish to work with the Student Disability Access Center to discuss a range of options to removing barriers in this course, including official accommodations. Please visit their website for information on this process and to apply for services online: sdac.studenthealth.virginia.edu. If you have already been approved for accommodations through SDAC, please send me your accommodation letter and meet with me so we can develop an implementation plan together.

5.3 Discrimination and power-based violence

The University of Virginia is dedicated to providing a safe and equitable learning environment for all students. To that end, it is vital that you know two values that I and the University hold as critically important

1. Power-based personal violence will not be tolerated.
2. Everyone has a responsibility to do their part to maintain a safe community on Grounds.

If you or someone you know has been affected by power-based personal violence, more information can be found on the UVA Sexual Violence website that describes reporting options and resources available - www.virginia.edu/sexualviolence.

As your professor and as a person, know that I care about you and your well-being and stand ready to provide support and resources as I can. As a faculty member, I am a responsible employee, which means that I am required by University policy and federal law to report what you tell me to the University’s Title IX Coordinator. The Title IX Coordinator’s job is to ensure that the reporting student receives the resources and support that they need, while also reviewing the information presented to determine whether further action is necessary to ensure survivor safety and the safety of the University community. If you wish to report something that you have seen, you can do so at the Just Report It portal. The worst possible situation would be for you or your friend to remain silent when there are so many here willing and able to help.

5.4 Religious accommodations

It is the University’s long-standing policy and practice to reasonably accommodate students so that they do not experience an adverse academic consequence when sincerely held religious beliefs or observances conflict with academic requirements.

Students who wish to request academic accommodation for a religious observance should submit their request in writing directly to me via Email (yangfeng@virginia.edu) or post it on Piazza as far in advance as possible. Students who have questions or concerns about academic accommodations for religious observance or religious beliefs may contact the University’s Office for Equal Opportunity and Civil Rights (EOCR) at UVAEOCR@virginia.edu or 434-924-3200.

Section 5.1 - 5.4 are adapted from a SEAS-wide example

Last updated on 08/16/2020