Advanced Topics in Natural Language Processing

Lectures and Schedules

Date Lecture Reading Assignment
08/24 Overview Reading on Embeddings
08/26 Language Encoding and Decoding Reading on DNN for NLP
08/31 Multilingual Multimedia Encoding
09/02 Multimedia Encoding Reading on Joint IE
09/07 Frontiers of Information Extraction
09/09 Frontiers of Information Extraction Reading on Multilingual Multimedia IE Assignment 1 on Multimedia Representation (due on 09/23)
09/14 Biomedical Information Extraction
09/16 Schema Induction Reading on Open IE and Schema Induction
09/21 Prompt Learning
09/23 Misinformation Detection Reading on Misinformation and QA Assignment 2 on Prompt Learning for Event Detection (due on 10/07)
09/28 Misinformation Detection Project Proposal Due 10/20, see Project Proposal Requirement
09/30 Misinformation Detection Reading on Knowledge Conditioned NLG
10/05 Knowledge Controlled Natural Language Generation Reading on Summarization
10/07 Abstractive Summarization Reading on Dialogue Systems
10/12 Dialog Systems
10/14 Fine-grained Knowledge Extraction
10/19 Project proposal presentation Assignment 3 on Fine-Grained Entity Typing (due on 11/04)
10/21 Project proposal presentation (Cont')
10/26 Project proposal presentation (Cont')
10/28 Question Answering (Guest Lecture by Dr. Avi Sil) Reading on Reading Comprehension Assignment 4 on Question Answering (Optional, 10 extra points) (due on 11/11)
11/02 Deep Learning from Graphs for NLP Reading on Deep learning from Graphs for NLP
11/04 Deep Learning from Graphs for NLP
11/09 Jordan Boyd-Graber's Colloquium on NLP Interpretability
11/11 Computational Social Science NLP and Computational Social Science
11/16 Code and Language
11/18 Guest Lecture by Prof. Snigdha Chaturvedi
11/19 Fall Break (No Class)
11/23 Fall Break (No Class)
11/25 Fall Break (No Class)
11/30 No Class
12/02 No Class, at NeurIPS
12/07 Extended class 3pm-5pm Final Project Presentations
12/09 Extended class 3pm-5pm Final Project Presentations Final Paper Due Dec 15

Course Description

In this course we will teach advanced topics in natural language processing, ranging from general techniques such as deep learning for NLP to specific topics such as information extraction, question answering, reading comprehension, summarization, dialogue systems, and natural language generation. Review of classic as well as state-of-the-art techniques and remaining challenges, and exploration of recent proposals for meeting these challenges. Intended for graduate students doing research in natural language processing.

 

Grading

Course Materials

The instructor will write most of the slides and hand-outs, give survey about the best papers from

The following books may provide some useful background:

Prerequisites

Class Policy

Mailing List

Office Hours and Paper Critique