Computational Linguistics 2

I teach LING 4434: Computational Linguistics 2. While LING 4424: Computational Linguistics 1 focuses on symbolic computational linguistics methods (n-gram smoothing, hidden markov modeling, probabilistic context-free grammars, etc), CL2 focuses on neural networks. Specifically, it focuses on techniques for inferring the linguistic knowledge encoded in neural network language models. This course is a work in progress, so any feedback/suggestions are appreciated.




Weeks 1-3: Background Lectures

Neural network basics/history
PyTorch overview
Neural network architectures

Week 4: Behavioral analyses

Linzen et al. (2016). Assessing the Ability of LSTMs to Learn Syntax-Sensitive Dependencies
Gulordava et al. (2018). Colorless Green Recurrent Networks Dream Hierarchically
Wilcox et al. (2018). What do RNN Language Models Learn about Filler–Gap Dependencies?
Chaves (2020). What Don't RNN Language Models Learn About Filler-Gap Dependencies?
Schuster et al. (2020). Harnessing the linguistic signal to predict scalar inferences

Week 5: Diagnostic classifiers

Giulianelli et al. (2018). Under the Hood
Qian et al. (2016). Analyzing Linguistic Knowledge in Sequential Model of Sentence
Adi et al. (2016). Fine-grained Analysis of Sentence Embeddings Using Auxiliary Prediction Tasks

Week 6: Adaptation-as-priming

Prasad et al. (2019). Using Priming to Uncover the Organization of Syntactic Representations in Neural Language Models
van Schijndel and Linzen (2018). A Neural Model of Adaptation in Reading
Lepori et al. (2020). Representations of Syntax [MASK] Useful: Effects of Constituency and Dependency Structure in Recursive LSTMs.

Weeks 7-8: Probe validation

Required (Week 7)
McCoy et al. (2019). Right for the Wrong Reasons
Required (Week 8)
Voita and Titov. (2020). Information-Theoretic Probing with Minimum Description Length
Hewitt and Liang. (2019). Designing and Interpreting Probes with Control Tasks
Pimentel et al. (2020). Information-Theoretic Probing for Linguistic Structure

Weeks 9-10: Group projects

Tuesdays: Project outlines/discussion
Thursdays: Group-suggested paper discussion

Group 1: NMT attention probing
Attention Tutorial
Reading: Wiegreffe and Pinter (2019). Attention is Not Not Explanation
Optional: Jain and Wallace (2019) Attention is Not Explanation

Reading: Response post by Byron Wallace
Project discussion

Group 2: Morphology in word-level LMs
Morphology Tutorial
Reading: Xu et al. (2018). Incorporating Latent Meanings of Morphological Compositions to Enhance Word Embeddings

Paper overview: Gulordava et al. (2018). Colorless Green Recurrent Networks Dream Hierarchically
Project discussion

Week 11: Misc probing

Tuesday Reading (RSA): Chrupala and Alishahi (2019). Correlating neural and symbolic representations of language
Thursday Reading (ablation; optional): Lakretz et al. (2019). The emergence of number and syntax units in LSTM language models

Weeks 12-13: Semi-finals (No class)

There are two CL conferences happening (virtually) during this period.
Registering for one gets the other free. Register here.
Cost: $100 (50 for EMNLP/CoNLL and 50 to become an ACL member)
Register by Oct 30.
The 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
The SIGNLL Conference on Computational Natural Language Learning (CoNLL)

Week 14: Poverty of the Stimulus

Bender and Koller. (2020). Climbing towards NLU
Davis & van Schijndel (2020). Recurrent Neural Network Language Models Always Learn English-Like Relative Clause Attachment
Bisk et al. (unpublished). Experience grounds language

Week 15: Misc Probing

Tuesday Reading (Iterated Learning - TA): Ren et al. (2020). Compositional Languages Emerge in a Neural Iterated Learning Model
Thursday: Mini-discussions of other topics of interest

Week 16

Project 2 presentations