• 08/12/2020: Welcome to the Natural Language Processing class (for graduate students and senior/advanced undergraduates)!
• 08/19/2020: Slides posted weekly; please check links below.
• 08/26/2020: Summary writing homeworks posted+emailed regularly.
• 09/11/2020: Coding HW1 (on word embedding training + visualization + evaluation) released — see details in email.
• 09/16/2020: Midterm project presentation details emailed.
• 10/01/2020: Midterm reports due to Oct10.
• 10/16/2020: Coding HW2 (on Sequence-to-Label Learning, Entailment Recognition) released — see details in email (due Oct30).

This course will be based on the artificial intelligence, machine learning, and linguistics field of natural language processing (NLP, or computational linguistics), to allow building automatic models that can analyze, understand, and generate text. We will cover important traditional topics of NLP such as tagging, parsing, coreference resolution, sentiment analysis, summarization, question-answering, and translation. We will also cover more recent topics of multimodal, grounded, and embodied semantics (i.e., language with vision and speech, for robotics), language generation and dialogue, and interpretable deep learning, etc.


Topics (tentative)

• Language Modeling
• Part-of-speech Tagging
• Syntactic Parsing: Constituent, Dependency, CCG, others
• Coreference Resolution
• Distributional Semantics: PMI, neural, CCA
• Compositional Semantics: Logical-form, Semantic Parsing, Vector-form, neural (RNNs/CNNs)
• Question Answering: Factoid-based, Passage-based
• Sentiment Analysis
• Document Summarization
• Machine Translation
• Dialogue Models
• Language and Vision: Image Captioning, Video Captioning, Visual Question Answering
• Language and Robotics: Instructions for Navigation, Manipulation, Skill Learning; Human-Robot Interaction
• Models: Deep+structured, interpretable, adversarial, reward-based (reinforcement learning), etc.
• Ethics and Bias in NLP/ML Models
• How to Write and Review Research Papers

• COMP 562 (Introduction to Machine Learning)
• A reasonable knowledge of Python programming language (preferably Pytorch/Tensorflow style libraries) and Linux program development environment.

Grading will (tentatively) consist of:

• Written homework and programming assignments (20\%),
• Midterm Project write-up and presentation (20\%)
• Final Project write-up and presentation (30\%)
• Paper presentations and written summaries (20\%)
• Class brainstorming and participation (10\%).

Students are allowed 3 free late days for assignments over the semester. After that, late assignments will be accepted with a 25\% reduction in value per day late.


Reference Books

• SLP2: Dan Jurafsky and James Martin. Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics and Speech Recognition. Prentice Hall, Second Edition, 2009
• SLP3: Some draft chapters of the third edition are available online.
• FSNLP: Chris Manning and Hinrich Schutze. Foundations of Statistical Natural Language Processing. MIT Press, Cambridge, MA: May 1999.
• Yoav Goldberg. Neural Network Methods for Natural Language Processing. Morgan Claypool, April 2017.
• Machine Learning Background: Andrew Ng's Coursera course
• Stanford NLP + Deep Learning Class

The professor reserves the right to make changes to the syllabus, including project due dates. These changes will be announced as early as possible.