# CSE 40657/60657 Natural Language Processing

Term
Fall 2021
Time
MWF 11:30–12:20
Room
118 DeBartolo
Instructor
David Chiang

Computers process massive amounts of information every day in the form of human language. Although they do not understand it, they can learn how to do things like answer questions about it, or translate it into other languages. This course is a systematic introduction to the ideas that form the foundation of current language technologies and research into future language technologies.

The official prerequisite is CSE 20312. Students should be experienced with writing substantial programs in Python. The course also makes use of finite automata, context-free grammars, basic linear algebra, multivariable differential calculus, and probability theory. Ideally, students should have taken CSE 30151, Math 10560, and ACMS 30440, but please contact the instructor if you have questions about the necessary background.

## Staff

The best way to contact the teaching staff is on Campuswire.

Instructor
Prof. David Chiang
Office hours: TTh 1–3pm
Zoom
Teaching assistant
Aarohi Srivastava
Office hours: Thu 5–7pm
150B Fitzpatrick
Teaching assistant
Gabriel Simoes
Office hours: Wed 7–9pm
Innovation Lounge, Duncan Student Center

## Schedule

Note: Readings and assignments in gray are from Spring 2021 and are still being updated.

 Week of Assignment Topic 08/23 Chapter 1 Project idea (due 09/03) Introduction 08/30 Chapter 2 Language models (n-grams) 09/06 HW1: Text prediction (due 09/17) Language models (RNNs) 09/13 Chapter 3 Machine translation (IBM models) 09/20 HW2: Machine translation (due 10/01) Machine translation (neural) 09/27 Chapter 4 Speech and writing 10/04 Chapter 5 Project baseline (due 10/15) Morphology, syntax, and parsing (CKY) 10/11 Parsing (neural) Fall Break 10/25 HW3: Parsing (due 11/05) MW: Semantics F: Projects 11/01 Chapter 6 MW: Classification F: Projects 11/08 HW4: Semantic parsing (due 11/19) MW: Sequence labeling F: Projects 11/15 MW: Generation F: Projects 11/22 HW5: Generation (due 12/03) Chapter 7 M: Projects 11/29 Projects 12/06 Project report (due 12/14) Conclusion

## Requirements

Your work in this course consists of five homework assignments and a research project.

All written work should be submitted through Sakai.

 requirement points homeworks 5 × 30 project 3 × 30 + 60 total 300
 letter grade points A 280–300 A− 270–279 B+ 260–269 B 250–259 B− 240–249 C+ 230–239 C 220–229 C− 210–219 D 180–209 F 0–179

## Policies

### Honor Code

Students in this course are expected to abide by the Academic Code of Honor Pledge: “As a member of the Notre Dame community, I will not participate in or tolerate academic dishonesty.”

The following table summarizes how you may work with other students and use print/online sources:

Resources Solutions allowed not allowed cite not allowed
See the CSE Guide to the Honor Code for definitions of the above terms.

If an instructor sees behavior that is, in his judgement, academically dishonest, he is required to file either an Honor Code Violation Report or a formal report to the College of Engineering Honesty Committee.

### Late Submissions

In the case of a serious illness or other excused absence, as defined by university policies, coursework submissions will be accepted late by the same number of days as the excused absence. Otherwise, you may submit part of an assignment on time for full credit and part of the assignment late with a penalty of 30% per week (that is, your score for that part will be $\lfloor 0.7^t s\rfloor$, where $s$ is your raw score and $t$ is the possibly fractional number of weeks late). No part of the assigment may be submitted more than once. No work may be submitted after the final project due date.

### Students with Disabilities

Any student who has a documented disability and is registered with Disability Services should speak with the professor as soon as possible regarding accommodations. Students who are not registered should contact the Office of Disability Services.

### COVID-19

During lectures, the current mask policy is: The instructor wears a mask and requires all students to do the same.

During in-person office hours, please respect the wishes of the instructor or TA with regard to masking and social distancing.

### Attendance

Although this is not a "dual-mode" course, attendance via Zoom is possible for legitimate reasons (for example, injury or illness, travel for sports or job interviews, need for greater social distancing). Please contact the instructor for more information.

### Classroom Recording

This semester all lectures will be recorded. The recordings can be accessed through Sakai using the Panopto tool.

Because we will be recording in the classroom, your questions and comments may be recorded. (Video recordings typically only capture the front of the classroom.) If you have any concerns about your voice or image being recorded, please speak to me to determine an alternative means of participating. No content will be shared with individuals outside of your course without your permission, except for faculty and staff that need access for support.