Advanced Methods in Natural Language Processing – Spring 2018

When: Tue, 13-16
Where: Orenstein 103
Instructor: Jonathan Berant
Graders: Ben Bogin (benb969), Mor Geva (mega.mor), Omri Koshorek (ko.omri), all at gmail.
Office hours: Coordinate by e-mail
Forum: Moodle

News
Overview

Natural Language Processing (NLP) aims to develop methods for processing, analyzing and understanding natural language. The goal of this class is to provide a thorough overview of modern methods in the field of Natural Language Processing. The class will not assume prior knowledge in NLP, and will mostly focus on methods from structured prediction and deep learning.

Prerequisites

Machine learning is a prerequisite for this class. If you want to attend and did not take any machine learning class or something equivalent (MOOCs do not count), you should talk to the instructor. Some assignments will include writing code in Python, while in others you are free to choose any programming language.

Grading
  1. Homework assignments: There will be 5 homework assignments that will constitute 50% of the final grade. Assignments should be submitted in triplets according to the instructions on the assignment. You get 5 late days throughout the semester and then it's 5 points per day per assignment.
  2. Project: A final project will constitute 50% of the final grade. There will be one or more default projects, where we will define an end task and you will build a model from scratch and run empirical experiments. You will be judged on the soundess of your model, empirical results, writing of final report, and code.
    You can also decide to do a research project, where you choose a research problem and attack it (see example projects from last year below). Research projects will also be judged on the choice of problem.
    Projects will be done in groups of three-four and will be presented in the last two classes (10 min. per group). You will submit a 6-page double-column report that summarizes your findings around September. Every late day will cause a deduction of 3 points. For research projects you will be judged also on the originality and merit of the proposed research. For research projects it is possible and recommended to have one project jointly with the advanced machine learning class. Projects done joinly with the ML class will be expected to be of high quality, striving towards a publication.
Recommended reading
Tentative schedule

Date Topic Reading Comments
6/3 Introduction
Word embeddings
word2vec, GloVe
13/3 Word embeddings Embeddings as matrix factorization Assign. 1
zipped code
Grades
20/3 Language models
neural LMs, FFNNs
Michael Collins' lecture notes, Neural embeddings, Backpropagation
10/4 Recurrent language models
RNNs, LSTMs, GRUs
more LSTM GRU
TensorFlow tutorial
Training RNNs, LSTMs Assign. 2
17/4 Tagging
Log-linear models
Michael Collins' lecture notes, Michael Collins' HMM notes, Michael Collins' LLM notes, MEMMs, FAQ
24/4 Globally-normalized linear models, Deep learning for tagging CRFs and label bias
Globally vs. locally normalized models
BiLSTM CRF for tagging
Assign. 3
1/5 Introduction to parsing
CKY
PCFG lecture notes
8/5 Lexicalized PCFGs
Syntactic parsing
Lexicalized PCFG lecture notes
Ratnaparkhi, 97;Hall et al., 14;
Shift-reduce parsing
Assign. 4
15/5 RST
Deep syntactic parsing
Semantic parsing intro
Neural CRF parsing, Minimal span-based neural parser
RNN grammars
22/5 Semantic parsing:
Compositionality
CCG
Learning
Parsing
Clarke et al., 2010, Liang et al., 2011, Artzi and Zettlemoyer, Berant et al., 2013, Berant and Liang, 2015 Assign. 5
29/5 Sequence to sequence
LSTM GRU
LSTM, seq2seq, GRU, Attention, Pointer networks, Jia and Liang, 2016, Weak supervision, Guu et al, 2017
5/6 Projects
12/6 Projects

Research projects from last year