What is this course about?This graduate-level course will focus on an advanced study of frameworks, algorithms and methods in NLP -- including state-of-the-art techniques for problems such as language modeling, text classification, machine translation, and question answering. The course will contain multiple programming assignments, paper readings, a mid-term and a final project. Students are expected to have taken at least one introductory course in NLP/machine learning prior to this class, and be comfortable with programming in Python.
Prerequisites:Students are expected to have taken at least one introductory course in NLP/machine learning (484/429 or similar) prior to this class, and be comfortable with programming in Python.
Time/location:All the lectures/precepts/office hours are held on Zoom and all the Zoom links can be found on Canvas.
|1||Fri (2/5)||Language Models||J&M, 3.5 - 3.6|
|2||Fri (2/12)||Text classification||Wang and Manning (2012)|
|3||Fri (2/19)||Word embeddings||Levy et al., (2015)|
|4||Fri (2/26)||Feedforward Neural Networks||Iyyer et al., (2015)|
|5||Fri (3/5)||Conditional Random Fields||Sha and Pereira (2003)|
|6||Fri (3/12)||No meeting (midterm)|
|7||Fri (3/19)||Recurrent neural networks and neural language models||Grave et al., (2017)|
|8||Fri (3/26)||Dependency parsing||Kiperwasser and Goldberg (2016)|
|9||Fri (4/2)||Machine translation||Sennrich et al., (2016)|
|10||Fri (4/9)||Transformers||Rush (2018)|
|11||Fri (4/16)||Pre-training||Yang et al., (2019)|
|12||Fri (4/23)||Language Grounding||Bisk et al., (2020)|