Advanced Topics in Natural Language Processing

Lectures and Schedules

Date Lecture Reading Assignment
08/25 Overview Reading on Embeddings
08/27 Language Encoding and Decoding Reading on DNN for NLP
09/01 Multilingual Multimedia Encoding
09/03 Deep Neural Networks for NLP Reading on Joint IE
09/08 Meta-learning for NLP
09/10 Joint Neural Models for Information Extraction Reading on Multilingual Multimedia IE Assignment 1 (due on 10/13)
09/15 Multilingual Information Extraction
09/17 Multimedia Information Extraction Reading on Open IE and Schema Induction
09/22 Open-domain Information Extraction
09/24 Schema Induction and Knowledge Acquisition Reading on Misinformation and QA
09/29 Misinformation Detection
10/01 Question Answering Reading on Knowledge Conditioned NLG
10/06 Natural Language Generation
10/08 Natural Language Generation Reading on Prompt Learning Project Proposal Due 10/20, see Project Proposal Requirement
10/13 Knowledge Controlled Natural Language Generation
10/15 Prompt Learning (guest lecture by Pengfei Liu (CMU)) Reading on Reading Comprehension Assignment 2 (due on 10/29)
10/20 Project proposal presentation
10/22 Project proposal presentation (Cont') Reading on Summarization
10/27 Project proposal presentation (Cont')
10/29 Reading Comprehension Reading on Dialogue Systems
11/03 Summarization Assignment 3 (due on 11/17)
11/05 Dialogue Systems Reading on Deep learning from Graphs for NLP
11/10 Guest lecture on Deep Learning from Graphs for NLP
11/12 Guest lecture on Deep Learning from Graphs for NLP NLP and Computational Social Science Assignment 4 (due on 12/03)
11/17 NLP and Computational Social Science
11/19 NLP and Computational Social Science (Cont')
11/24 Fall Break (No Class)
11/26 Fall Break (No Class)
12/01 Final Project Presentations Final project presentation 12/01 1pm CT
12/03 Final Project Presentations
12/08 Final Project Presentations
12/10 Final Project Presentations Final Paper Due

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.



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:


Class Policy

Mailing List

Office Hours and Paper Critique