Note: As per University policy, class will be held remotely for the first two weeks of the semester. Please use the Zoom link on Blackboard.

In natural language processing (NLP), we set out to solve language-related tasks (e.g., machine translation, question answering) but often evaluate on narrow, in-distribution test datasets. With recent advances in deep learning, modern systems have achieved high accuracy on many canonical datasets, but still seem far from solving general tasks. In this class, we will survey recent research on robustness and generalization that studies this gap between in-distribution accuracy and task competency through out-of-distribution settings. We will learn about different settings in which NLP systems often fail to generalize well, including adversarial perturbations, settings that require compositional reasoning, and domain transfer. We will also learn about how average accuracy can mask disparate performance across subpopulations, and how this can lead to undesirable consequences. Across these topics, we will cover methods both for measuring these robustness and generalization issues and ways that we can improve model robustness and generalization.



Familiarity with natural language processing and/or machine learning at the level of CSCI 544 (Applied Natural Language Processing) or CSCI 567 (Machine learning). Please email me if you want to enroll but are unsure if you meet the prerequisites.

For those without prior NLP experience, I recommend going through Lena Voita’s NLP Course For You, which provides a concise and interactive introduction to modern NLP. For a more extensive introduction to NLP, I recommend Jurafsky and Martin’s Speech and Language Processing, whose third edition is available online and is very current.


Date Topic Reading(s) Additional reading(s) Assignments
Mon Jan 10 Introduction      
Wed Jan 12 The Turing Test: Lecture Turing 1950, Shieber 2016 Shieber et al. 2004  
Mon Jan 17 No class (Martin Luther King Day)      
Wed Jan 19 Adversarial examples I: Lecture   Goodfellow et al. 2014, Adversarial ML Tutorial  
Mon Jan 24 Adversarial examples II: Adversarial Perturbations Pruthi et al. 2019, Jones et al. 2020 Ribeiro et al. 2018, Jia et al. 2019, Huang et al. 2019  
Wed Jan 26 Adversarial examples III: Adversarial triggers Wallace et al. 2019, Atanasova et al. 2020    
Mon Jan 31 Adversarial examples IV: Model stealing, data poisoning Krishna et al. 2020, Wallace et al. 2021 Wallace et al. 2020  
Wed Feb 2 Domain adaptation I: Lecture   Ramponi and Plank, 2020  
Mon Feb 7 Domain adaptation II: Unsupervised domain adaptation and pretraining Blitzer et al. 2006, Han and Eisenstein 2019 Gururangan et al. 2020  
Wed Feb 9 Domain adaptation III: Fair generalization tasks, empirical trends Geiger et al. 2019, Miller et al. 2020 Fisch et al. 2019, Taori et al. 2021 Project proposal due Feb 11
Mon Feb 14 Spurious correlations I: Lecture   Imbens and Rubin, 2015, Imbens 2020, Feder et al., 2021  
Wed Feb 16 Spurious correlations II: Dataset biases Schwartz et al. 2017, Gururangan et al. 2018, Gardner et al. 2021 Poliak et al. 2018, Kaushik et al. 2018, Schuster et al. 2019, Ribeiro et al. 2020  
Mon Feb 21 No class (Presidents’ Day)      
Wed Feb 23 Spurious correlations III: Training-time strategies Clark et al. 2019, Utama et al. 2020 Clark et al. 2020, Tu et al. 2020  
Mon Feb 28 Spurious correlations IV: Counterfactual data augmentation Kaushik et al. 2019, Joshi and He 2021 Gardner et al. 2020, Ross et al. 2021, Sen et al. 2021  
Wed Mar 2 Fairness I: Lecture   Barocas, Hardt, and Narayanan  
Mon Mar 7 Fairness II: Gender and race bias in NLP systems Zhao et al. 2018, Rudinger et al. 2018, Sap et al. 2019 Blodgett et al. 2020, Field et al. 2021  
Wed Mar 9 Fairness III: Bias in representations Goldfarb-Tarrant et al. 2021, Vig et al. 2020 Caliskan et al. 2017  
Mon Mar 14 No class (Spring break)      
Wed Mar 16 No class (Spring break)      
Mon Mar 21 Fairness IV: Distributionally robust optimization Hashimoto et al. 2018, Sagawa et al. 2020 Oren et al. 2019, Michel et al. 2021  
Wed Mar 23 Fairness V: Bias amplification Zhao et al. 2017, Jia et al. 2020 Wang et al. 2019 Project progress report due Mar 25
Mon Mar 28 Compositionality I: Lecture Fodor and Pylyshyn 1988 Coppock and Champollion, Szabó 2008  
Wed Mar 30 Compositionality II: Measuring compositional behavior Hupkes et al. 2020 Lake and Baroni 2018, Kim and Linzen 2020, Dankers et al. 2021  
Mon Apr 4 Compositionality III: Modeling choices Herzig et al. 2021, Csordás et al. 2021 Chen et al. 2020, Shaw et al. 2021, Furrer et al. 2021  
Wed Apr 6 Dataset creation I: Adversarial data collection Kaushik et al. 2021, Wallace et al. 2021 Wallace et al. 2019, Kiela et al. 2021  
Mon Apr 11 Dataset creation II: Adversarial filtering Le Bras et al. 2020, Phang et al. 2021 Swayamdipta et al. 2020  
Wed Apr 13 Conclusion, Bonus topics      
Mon Apr 18 Project presentations      
Wed Apr 20 Project presentations      
Mon Apr 25 Project presentations      
Wed Apr 27 Project presentations     Project final report due May 6


Class days marked as Introduction, Conclusion, or Lecture will be presentations by me. Other classes will be paper presentations and discussions led by 1-2 students. The expected format of these classes is:


Grades will be based on paper presentations (30%), discussion (10%), and a final project (60% total).

Paper presentations (30%). Students will be expected to present ~2 research papers (sometimes 3 short ones or 1 long one) and lead class discussion on these papers. The presentation should help everyone in the class understand these papers as well as relevant background material. The presenter should also prepare a few discussion questions to encourage discussion after the presentation. To help presenters prepare their presentations, each presentation day will also have an assigned proofreader. The presenter should send a draft of the presentation and discussion questions to the proofreader at least 48 hours in advance of the presentation, and the proofreader should give some feedback at least 24 hours in advance.

Paper discussion participation (10%). Students are expected to participate in class discussions. This includes asking questions during presentations as well as voicing opinions on discussion topics.

Final project (60% total). Students must complete a final research project on a topic related to the class. Projects may be conducted individually or in groups of two. This project is expected to include novel research on either evaluation methodology for identifying problems with models related to robustness, generalization, or fairness, or modeling innovations for improving robustness, generalization, fairness, or other related aspects of model behavior. Please come to office hours or email me if you have questions related to choosing a project direction.

Final project

The final project is worth 60% of the total grade. Points will be allocated as follows:

Project proposal (5%). Students should submit a ~2-page (minimum) proposal for their project by the end of Week 5 (February 11). The proposal should describe the goal of the project and include a survey of related work. When reading these proposals, I will be looking for the following:

Project progress report (10%). Students should submit a ~5-page progress report for their project by the end of Week 10 (March 25). This should once again describe the project’s goals (which may have changed since the proposal), initial results, and a concrete plan of what will be done for the final report. While the initial results need not be positive, students are expected to have made non-trivial implementation progress by this point. For parts of the report describing project goals and plans, the expectations are largely the same as for the proposal. In addition, I will be looking for the following:

Project final presentation (20%). This will be a 20-30 minute presentation during the last two weeks of class. Students should describe the motivation for their work, relevant background material, and results. I encourage students to present both positive and negative results. There will also be some time for audience questions.

Project final report (25%). Students should submit a ~8-page final report detailing all aspects of their project (due May 6). The report should be structured like a conference paper. Parts of the proposal and progress report may be reused for the final report.

All written project-related assignments should use the standard *ACL paper submission template (Log in to Overleaf and go to Menu -> Copy Project). All due dates are 11:59pm PST on Friday.

Late days

You are given 4 late days to use for the project proposal and progress report (no late days for the final report), to be used in integer amounts and distributed as you see fit. Additional late days will result in a deduction of 10% of the grade on the corresponding assignment per day.

Project resources

Google Colab provides free computational resources, though there are limits (e.g., jobs can only run for 12 hours at a time). See their FAQ for details.