CSE 291: Statistical Natural Language Processing

Term: Spring Qtr 2017
Instructor: Ndapa Nakashole, EBU3B 4108
Lecture: Tuesday and Thursday 3:30pm-4:50pm, EBU3B 2154
Credits: 4
Office Hours: Monday 1pm - 2pm; Tuesday and Thursday 4:50pm-5.30pm



Course Description

This is a research-oriented course on statistical natural language processing (NLP). The course will focus on understanding and extending state-of-the-art machine learning algorithms for a series of important topics in NLP. The course involves reading and discussing current research papers. This course assumes background in basic machine learning. Prior NLP experience is helpful, but not required.



Course Format

Class Project

For the class project, your task is to write a 4-6 page paper, written by you alone, to extend a past NLP research contribution.
Pick a NLP paper among class readings or any paper from NLP and ML conferences.
Formatting: Please use the ACL 2017 style files. Papers longer than 6 pages of the ACL 2017 format will not be considered.
Your class paper should describe:

Recommended texts are:

[J&M] 3rd edition free chapters online
[M&S] is free online.

Machine Learning background: Andrew Ng's Coursera course

Syllabus (Tentative, subject to substantial change!)
Date Topic/Readings Discussion Leader Homework
Apr 6 Introduction
J&M Chapter 1 Ndapa Nakashole
Representation Learning for NLP
Apr 11 Goldberg, JAIR 2016 A Primer on Neural Network Models for NLP. Sections 3 & 5 (Background) Ndapa Nakashole
Mikolov et al., NIPS 2013 Distributed Representations of Words and Phrases and their Compositionality Review (04/10)
Le and Mikolov, ICML 2014 Distributed Representations of Sentences and Documents (Optional)
Machine Reading, & QA
Apr 13 Sukhbaatar et al., NIPS 2015 End-To-End Memory Networks Ndapa Nakashole Review (04/12)
Weston et al., ICLR 2015 Memory Networks (Background)
Apr 18 Carlson et al., AAAI 2010 Toward an Architecture for Never-Ending Language Learning Ndapa Nakashole Review (04/17)
Mitchell et al., AAAI 2015 Never-Ending Learning
Apr 20 Herman et al., NIPS 2015 Teaching Machines to Read and Comprehend Ndapa Nakashole Review (04/19)
Chen et al., ACL 2016 A Thorough Examination of the CNN/Daily Mail Reading ...
Cross-lingual projection, & Morphology
Apr 25 Lazaridou et al., ACL 2015 Delving into Cross-Space Mapping for Zero-Shot Learning Ndapa Nakashole Review (04/24)
Hermann et al., ACL 2014 Multilingual Models for Compositional Distributed Semantics
Apr 27 Soricut and Och, NAACL 2015 Unsupervised Morphology Induction Using Word Embeddings Ndapa Nakashole Review (04/26)
May 2 Kiros et al., NIPS 2015 Skip-Thought Vectors Shuai Tang Review (05/01)
Hill et al., NAACL 2016 Learning Distributed Representations of Sentences from Unlabelled Data
May 4 Bowman, et al., ACL 2016 A Fast Unified Model for Parsing and Sentence Understanding Mingyang Wang Review (05/03)
Yogatoma et al., ICLR 2017 Learning to Compose Words into Sentences using Reinforcement Learning
Part-of-Speech Tagging, NER, & Phonology
May 9 Ling et al., EMNLP 2015 Finding Function in Form: Compositional Character Models for ... Shashankar Sudarsan Review (05/08)
May 11 Chiu and Nichols, TACL 2016 Named Entity Recognition with Bidirectional LSTM-CNNs Kan Xu Review (05/10)
Goldberg, JAIR 2016 A Primer on Neural Net Models for NLP. Sec 9, pp. 41-45 (CNNs Background)
Kim, EMNLP 2014 Convolutional Neural Networks for Sentence Classification (Optional)
Rativov, CONLL 2009 Design Challenges and Misconceptions in Named Entity Recognition (Optional)
May 16 Tsvetkov et al., NAACL 2016 Polyglot Neural Language Models: A Case Study in ... Sindhura Raghavan Review (05/15)
Sentiment Analysis
May 18 Socher et al., EMNLP 2013 Recursive Deep Models for Semantic Compositionality ... Utkarsh Simha Review (05/17)
Irsoy and Cardie, ACL 2014 Opinion Mining with Deep Recurrent Neural Networks Caroline Kim
Machine Translation
May 23 Sutskever, et al., NIPS 2014 Sequence to Sequence Learning with Neural Networks Nishant Gurnani Review (05/22)
Bahdanau et al., ICLR 2015 Neural Machine Translation by Jointly Learning to Align and Translate Mainak Biswas
May 25 Lee, et al., TACL 2017 Fully Character-Level Neural Machine Translation... Rishikesh Ghewari Review (05/24)
Wu et al., arxiv 2016 Google's Neural Machine Translation System: Bridging the ... Mridul Garg
Structured Prediction
May 30 Collins, EMNLP 2002 Theory and Experiments with Perceptron Algorithms Cuong Luong Review(05/29)
Dong and Lapata, ACL 2016 Language to Logical Form with Neural Attention Swathi V. Hoysala
Jun 1 Xu et al., ICML 2015 Neural Image Caption Generation with ... Steven Hill Review (05/31)
Ortiz et al., NAACL 2015 Learning to Interpret and Describe Abstract Scenes Yuting Wang
Jun 6 Venugopalan et al., CVPR 2017 Captioning Images with Diverse Objects Dustin Wright Review (05/04)
Mei et al., AAAI 2016 Mapping of Navigational Instructions to Action Sequences Vraj Shah
Jun 8 TBD