CSCI 5541, NLP
Spring 2023, Monday and Wednesday, 4:00pm to 5:15pm, Mechanical Engineering 108
Natural Language Processing (NLP) is an interdisciplinary field that is based on theories in linguistics, cognitive science, and social science. The main focus of NLP is building computational models for applications such as machine translation and dialogue systems that can then interact with real users. Research and development in NLP therefore also includes considering important issues related to real-world AI systems, such as bias, controllability, interpretability, and ethics. This course will cover a broad range of topics related to NLP, from theories to computational models and applications to data annotation and evaluation, leading to in-depth discussions with students. Students will read papers on those topics, create an annotated dataset, and implement algorithms on applications they are interested in. There will be a semester-long class project where you collect your own dataset, ensure it is accurate, develop a model using existing computing tools, evaluate the system, and consider its ethical and societal impacts. The grade will be evaluated based on the course project, participation, and assignments.
8980 vs 5980 vs 5541: Some lectures across the three classes will be shared but they have different focuses; 5980 (NLP with Deep Learning) focuses on more "processing" parts of NLP, particularly with deep learning methods. Students will gain an instruction to cutting-edge techniques in deep learning for NLP. 8980 (Intro to NLP Research) covers broad aspects of NLP research as an interdisciplinary problem, including theory grounding, data annotation, error analysis, and applications to different fields. 5541 (NLP, current course) is an introductory class to cover some basic NLP techniques with applications such as question answering, dialogue, and machine translation.
- Dongyeop Kang (a.k.a DK)
- Class meets
- Monday and Wednesday, 4:00pm to 5:15pm, Mechanical Engineering 108
- Debarati Das (email@example.com)
- Risako Owan (firstname.lastname@example.org)
- Office hours
- DK: Friday, 4pm to 5pm in Shepherd 259
- TAs: Tuesday and Thursday, 3pm to 4pm via Zoom
- Class page
Grading and Late Policy
- 65% Homeworks (total five homeworks)
- 25% Project
- 10% Class Participation
- Active participation in class discussion and project presentations
Late policy for deliverablesEach student will be granted 3 late days to use for homeworks over the duration of the semester. After all free late days are used up, penalty is 25% for each additional late day. However, projects submitted late after all late days have been used will receive no credit.
We will cover basic NLP representations and applications, with some advanced topics. Pleaes pay attention to due dates and project presentations.
Homework Details (65%)
Collaboration is required (maximum of 4 people). Questions should be communicated with TAs, and please use the shared Slack channels (e.g., #hw1) to share them with others. The use of outside resources (books, research papers, websites, etc.) or collaboration (students, professors, chatGPT, etc.) must be explicitly acknowledged. Check out the notes to students.
- HW0: Building a text classifier with pytorch from scratch (0 points, due: Jan 30) (link)
- HW1: Finetuning a text classifier using HuggingFace (15 points, due:
Feb 13Feb 15) (link)
- HW2: Building ngram language models (LM) from scratch (15 points, due: Mar 1) (link)
- HW3: Generating text from pretrained LMs (15 points, due:
Mar 20 Mar 22March 24) (link)
- HW4: Prompting with large language models (LLMs) (10 points, due:
Apr 3Apr 7) (link)
- HW5: Evaluating NLP systems (10 points, due: Apr 17) (link)
Project Details (25%)
Please carefully read the project description (link) first. Every group member (maximum of 4 people) should submit their report, link to code (or a zipped code), and presentation slides/poster on Canvas before the deadline. Your project will be evaluated in the following criteria:
- Proposal report (5 points, due:
Mar 13Mar 17)
- Midterm office hour participation (5 points, due: Apr 10)
- Final report and poster poresentation (15 points, due: May 5)
- Critical analysis of existing model/dataset (default project),
- New research results judged suitable for acceptance to a NLP or ML workshop,
- Collection of your own dataset on new problems or adversarial datasets that can fool the existing systems ,
- An in-depth literature survey on emerging topics,
- Interactive demonstration (e.g., Chrome Extension, Flask) or visualization of existing systems,
- New open-source repository or dataset with a high impact on the community
- Understanding Narrative Transportation in Fantasy Fanfiction (Kelsey Neis and Yu Fang), CSCI 8980 S22
- Exploring Episodic Memory through Cross-modal representations (Abhiraj Mohan, Emily Mulhall, Jayant Sharma), CSCI 8980 S22
- Generating Controllable Long-dialogue with Coherence (Zhecheng Sheng, Chen Jiang, Tianhao Zhang), CSCI 5980 F22
- Cross-lingual Transfer Learning for Irony Detection (Chen Hu, Jiaqi Liu, Keyang Xuan), CSCI 5980 F22
Required: CSCI 2041 Advanced Programming Principles
Recommended: CSCI 5521 Introduction to Machine Learning or any other course that covers fundamental machine learning algorithms.
Furthermore, this course assumes:
- Good coding ability, corresponding to at least a third or fourth-year undergraduate CS major. Assignments will be in Python.
- Background in basic probability, linear algebra, and calculus.
Notes to students
Assignments and project reports for the class must represent individual effort unless group work is explicitly allowed. Verbal collaboration on your assignments or class projects with your classmates and instructor is acceptable. But, everything you turn in must be your own work, and you must note the names of anyone you collaborated with on each problem and cite resources that you used to learn about the problem. If you have any doubts about whether a particular action may be construed as cheating, ask the instructor for clarification before you do it. Cheating in this course will result in a grade of F for course and the University policies will be followed.
Students with Disabilities
If you have a disability for which you are or may be requesting an accommodation, you are encouraged to contact both your instructor and Disability Resources Center (DRC).
All students are expected to abide by campus policies regarding COVID-19 including masking and vaccination requirements. This is an in-person class with daily in-person activities, but we may consider a hybrid or online option. If you're feeling sick, stay at home and catch up with the course materials instead of coming to class!
Textbook / Related Classes / Online Resources
BookTextbook is not required but the following books are primarily referred:
- Jurafsky and Martin, Speech and Language Processing, 3rd edition [online]
- Jacob Eisenstein. Natural Language Processing