Fall 2022

LIN/CSE 667: Advanced Topics in Computational Linguistics

Instructor Name: Dr. Cassandra Jacobs Class Day and Time: MW 9:00-10:20AM Number of Credits: 1-3 units Email Address: cxjacobs@buffalo.edu Office Location: 614 Baldy Hall Office Hours: On Zoom by appointment

Course description

This course aims to provide students with an overview of the key areas which make up the field called Computational Linguistics, an understanding of the major challenges of the field as well as the major application areas for language processing techniques, and the skills to implement fundamental language processing algorithms. This course is dual listed between CSE 667LEC and LIN 667LEC.

Required Text and Materials

All reading materials will be made available on UBLearns as well as the course webpage and will consist primarily of journal articles or conference proceedings.

Theme: Linguistic probes and linguistic representations in large language models

The landscape for natural language processing (NLP) has changed dramatically in the past decade with the explosion of neural language models for downstream NLP tasks. A persistent challenge, however, is that prior methods, e.g., structured perceptron approaches, random forests, etc., were significantly more transparent from the perspective of the modeler. Given the nature of the model representations, it was trivial to inspect why a classifier made the decision it did and cheap and easy to perform causal inference by performing feature ablation. While deep learning largely rejects interpretability, it is of great importance to scientists and practitioners of NLP to be able to understand what is encoded in neural language models concretely, e.g., linguistic or statistical regularities. This course is a deep dive into the literature on probing for linguistic factors (e.g., syntactic structure) in the decisions of neural language models, in addition to the statistical regularities that they encode (e.g., linguistic bias).

Goals

Weekly class/lecture structure

The course is primarily conducted using a mix of instructor-guided discussion and student-led discussion. Each week on Wednesdays, we will discuss a broad linguistic domain relevant to doing NLP with deep learning, why interpretability is critical for that domain, and the technological tools that have been developed to probe different structures in that linguistic domain. Particular attention will be paid to the nature of the datasets leveraged or generated during lecture. On Mondays, two students will each present one paper from the current theme. Then, by the following Wednesday, every non-presenter will submit a review for papers from the preceding week.

Grade composition

Weekly paper reviews

As part of this course, you will be asked to provide an ACL Rolling Review-style paper review for one of two selected papers from each week. The goal of these assignments is to encourage you to analyze research work from a scientific perspective. Do not be afraid if the task is challenging — it is expected that the quality of your reviews will improve over the course of the semester as you gain familiarity with the literature and obtain feedback on your reviews. More details are available here: https://aclrollingreview.org/reviewform

Reading Schedule

• Week 1 (August 29-): Interpretability and probes

  ✏️Week 1 lecture notes

  • August 29 - Introduction to the course and interpretability

  • August 31 - Instructor-guided discussion on Probing and Probing Tasks

    • Alishahi, A., Chrupała, G., & Linzen, T. (2019). Analyzing and interpreting neural networks for NLP: A report on the first BlackboxNLP workshop. Natural Language Engineering, 25(4), 543-557. https://www.cambridge.org/core/journals/natural-language-engineering/article/analyzing-and-interpreting-neural-networks-for-nlp-a-report-on-the-first-blackboxnlp-workshop/FAFF1B645BBF89FE400A521526AA65D4

    • Rogers, A., Kovaleva, O., & Rumshisky, A. (2020). A primer in BERTology: What we know about how BERT works. Transactions of the Association for Computational Linguistics, 8, 842-866. https://direct.mit.edu/tacl/article/doi/10.1162/tacl_a_00349/96482/A-Primer-in-BERTology-What-We-Know-About-How-BERT

    • Andreas Madsen, Siva Reddy, and Sarath Chandar. 2022. Post-hoc Interpretability for Neural NLP: A Survey. ACM Computing Surveys. Just Accepted (June 2022). https://doi.org/10.1145/3546577

• Week 2 (September 5-): Interpreting Neural NLP models

  ✏️Week 2 lecture notes

  • September 5: Labor Day! No class!

  • September 7: Instructor-guided discussion on Interpreting Neural NLP - Discussion due September 6 at 10pm

    • Sarthak Jain and Byron C. Wallace. 2019. Attention is not Explanation. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 3543–3556, Minneapolis, Minnesota. Association for Computational Linguistics. https://aclanthology.org/N19-1357/

    • Sarah Wiegreffe and Yuval Pinter. 2019. Attention is not not Explanation. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 11–20, Hong Kong, China. Association for Computational Linguistics. https://aclanthology.org/D19-1002/

    • Tenney, I., Das, D., & Pavlick, E. (2019, July). BERT Rediscovers the Classical NLP Pipeline. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (pp. 4593-4601). https://aclanthology.org/P19-1452/

    • Durrani, N., Sajjad, H., Dalvi, F., & Belinkov, Y. (2020, November). Analyzing Individual Neurons in Pre-trained Language Models. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (pp. 4865-4880). https://aclanthology.org/2020.emnlp-main.395/

• Week 3 (September 12-): Syntactic Structure 1

  ✏️Week 3 lecture notes

  Python code for using transformers to probe model’s self-attentions across layers

  • September 12: Instructor-guided discussion on grammatical categories & in-class discussion of week 2 readings

  • September 14: Instructor-guided discussion on larger syntactic structures - Discussion due September 13 at 10pm - In-class “peer review” walkthrough for Week 5 assignment

    • Kim, N., & Smolensky, P. (2021, February). Testing for Grammatical Category Abstraction in Neural Language Models. In Proceedings of the Society for Computation in Linguistics 2021 (pp. 467-470). https://aclanthology.org/2021.scil-1.59/ - Instructor review / presentation demo

    • Kim, N., Rawlins, K., Van Durme, B., & Smolensky, P. (2019, July). Predicting the argumenthood of English prepositional phrases. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 33, No. 01, pp. 6578-6585). https://dl.acm.org/doi/abs/10.1609/aaai.v33i01.33016578

    • Marius Mosbach, Stefania Degaetano-Ortlieb, Marie-Pauline Krielke, Badr M. Abdullah, and Dietrich Klakow. 2020. A Closer Look at Linguistic Knowledge in Masked Language Models: The Case of Relative Clauses in American English. In Proceedings of the 28th International Conference on Computational Linguistics, pages 771–787, Barcelona, Spain (Online). International Committee on Computational Linguistics. https://aclanthology.org/2020.coling-main.67

    • Gulordava, K., Bojanowski, P., Grave, É., Linzen, T., & Baroni, M. (2018, June). Colorless Green Recurrent Networks Dream Hierarchically. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) (pp. 1195-1205). https://aclanthology.org/N18-1108/

  • Start thinking about your final projects!

• Week 4 (September 19-): Syntactic Structure 2

  • September 19: Instructor-guided discussion on syntactic processes

  • [No paper review due this week]

  • September 21: Instructor-guided discussion on syntactic processes - Discussion due September 20 at 10pm

    • Aina, L., & Linzen, T. (2021, November). The Language Model Understood the Prompt was Ambiguous: Probing Syntactic Uncertainty Through Generation. In Proceedings of the Fourth BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP (pp. 42-57). https://aclanthology.org/2021.blackboxnlp-1.4/ - Arthur Domino

    • Finlayson, M., Mueller, A., Gehrmann, S., Shieber, S. M., Linzen, T., & Belinkov, Y. (2021, August). Causal Analysis of Syntactic Agreement Mechanisms in Neural Language Models. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) (pp. 1828-1843). https://aclanthology.org/2021.acl-long.144/

    • Zhang, Y. (2020, November). Latent Tree Learning with Ordered Neurons: What Parses Does It Produce?. In Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP (pp. 119-125). https://aclanthology.org/2020.blackboxnlp-1.11/

    • Adina Williams, Andrew Drozdov*, Samuel R. Bowman; Do latent tree learning models identify meaningful structure in sentences?. Transactions of the Association for Computational Linguistics 2018; 6 253–267. https://doi.org/10.1162/tacl_a_00019

• Week 5 (September 26-): Multilingual representations

  • September 26: Student-led discussion on Syntactic Structure 2 - Recorded, watch on your own

    • Arthur Domino

  • September 26: Syntactic Structure 1 paper review due

  • September 28: Instructor-guided discussion on multilingual models - Recorded, watch on your own

    • Ethan A. Chi, John Hewitt, and Christopher D. Manning. 2020. Finding Universal Grammatical Relations in Multilingual BERT. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 5564–5577, Online. Association for Computational Linguistics. https://aclanthology.org/2020.acl-main.493/ - Sabiha Shaik

    • Taraka Rama, Lisa Beinborn, and Steffen Eger. 2020. Probing Multilingual BERT for Genetic and Typological Signals. In Proceedings of the 28th International Conference on Computational Linguistics, pages 1214–1228, Barcelona, Spain (Online). International Committee on Computational Linguistics. https://aclanthology.org/2020.coling-main.105/

    • Tanti, M., van der Plas, L., Borg, C., & Gatt, A. (2021, November). On the Language-specificity of Multilingual BERT and the Impact of Fine-tuning. In Proceedings of the Fourth BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP (pp. 214-227). https://aclanthology.org/2021.blackboxnlp-1.15/

    • Blevins, T., & Zettlemoyer, L. (2022). Language Contamination Explains the Cross-lingual Capabilities of English Pretrained Models. arXiv preprint arXiv:2204.08110. https://arxiv.org/abs/2204.08110 - Sarah Sues

  • Email CJ about your final project plans!

• Week 6 (October 3-): Semantic Knowledge 1

  • October 3: Student-led discussion on Multilingual representations

    • Sarah Sues

    • Sabiha Shaik

  • October 3: Syntactic Structure 2 paper review due

  • October 5: Instructor-guided discussion on lexical semantics - Discussion due October 4 at 10pm

    • Ivan Vulić, Edoardo Maria Ponti, Robert Litschko, Goran Glavaš, and Anna Korhonen. 2020. Probing Pretrained Language Models for Lexical Semantics. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 7222–7240, Online. Association for Computational Linguistics. https://aclanthology.org/2020.emnlp-main.586

    • Garí Soler, A., & Apidianaki, M. (2021). Let’s play mono-poly: BERT can reveal words’ polysemy level and partitionability into senses. Transactions of the Association for Computational Linguistics, 9, 825-844. https://direct.mit.edu/tacl/article-abstract/doi/10.1162/tacl_a_00400/106797 - Jacob Springborn

    • Aina Garí Soler and Marianna Apidianaki. 2020. BERT Knows Punta Cana is not just beautiful, it’s gorgeous: Ranking Scalar Adjectives with Contextualised Representations. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 7371–7385, Online. Association for Computational Linguistics. https://aclanthology.org/2020.emnlp-main.598/ - Benjamin Conrow-Graham

• Week 7 (October 10-): Semantic Knowledge 2

  • October 10: Student-led discussion on Semantic Knowledge 1

    • Jacob Springborn

    • Benjamin Conrow-Graham

  • October 10: Multilingual representations paper review due

  • October 12: Instructor-guided discussion on reasoning - Discussion due October 11 at 10pm

    • Nora Kassner, Benno Krojer, and Hinrich Schütze. 2020. Are Pretrained Language Models Symbolic Reasoners over Knowledge?. In Proceedings of the 24th Conference on Computational Natural Language Learning, pages 552–564, Online. Association for Computational Linguistics. https://aclanthology.org/2020.conll-1.45/

    • Bhagavatula, C., Le Bras, R., Malaviya, C., Sakaguchi, K., Holtzman, A., Rashkin, H., ... & Choi, Y. (2019, September). Abductive Commonsense Reasoning. In International Conference on Learning Representations. https://openreview.net/forum?id=Byg1v1HKDB

    • Nora Kassner and Hinrich Schütze. 2020. Negated and Misprimed Probes for Pretrained Language Models: Birds Can Talk, But Cannot Fly. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 7811–7818, Online. Association for Computational Linguistics. https://aclanthology.org/2020.acl-main.698 - Venkata Sai Rohit Ayyagari

• Week 8 (October 17-): Fine-tuning

  • October 17: Student-led discussion on Semantic Knowledge 2

    • Venkata Sai Rohit Ayyagari

  • October 17: Semantic Knowledge 1 paper review due

  • October 19: Instructor-guided discussion on fine-tuning language models - Discussion due October 18 at 10pm

    • Merchant, A., Rahimtoroghi, E., Pavlick, E., & Tenney, I. (2020, November). What Happens To BERT Embeddings During Fine-tuning?. In Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP (pp. 33-44). https://aclanthology.org/2020.blackboxnlp-1.4/ - Sean Afridi

    • Marius Mosbach, Anna Khokhlova, Michael A. Hedderich, and Dietrich Klakow. 2020. On the Interplay Between Fine-tuning and Sentence-level Probing for Linguistic Knowledge in Pre-trained Transformers. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 2502–2516, Online. Association for Computational Linguistics. https://aclanthology.org/2020.findings-emnlp.227/

    • Durrani, N., Sajjad, H., & Dalvi, F. (2021, August). How transfer learning impacts linguistic knowledge in deep NLP models?. In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (pp. 4947-4957). https://aclanthology.org/2021.findings-acl.438/ - Shubham Pandey

    • Yu, L., & Ettinger, A. (2021, August). On the Interplay Between Fine-tuning and Composition in Transformers. In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 (pp. 2279-2293). https://aclanthology.org/2021.findings-acl.201/

• Week 9 (October 24-): Bias 1

  • October 24: Student-led discussion on Fine-tuning

    • Sean Afridi

    • Shubham Pandey

  • October 24: Semantic Knowledge 2 paper review due

  • October 26: Instructor-guided discussion on identifying bias - Discussion due October 25 at 10pm

    • Ethayarajh, K., Duvenaud, D., & Hirst, G. (2019, July). Understanding Undesirable Word Embedding Associations. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (pp. 1696-1705). https://aclanthology.org/P19-1166/

    • Victor Steinborn, Philipp Dufter, Haris Jabbar, and Hinrich Schuetze. 2022. An Information-Theoretic Approach and Dataset for Probing Gender Stereotypes in Multilingual Masked Language Models. In Findings of the Association for Computational Linguistics: NAACL 2022, pages 921–932, Seattle, United States. Association for Computational Linguistics. https://aclanthology.org/2022.findings-naacl.69/

    • Zhao, J., Wang, T., Yatskar, M., Ordonez, V., & Chang, K. W. (2018, June). Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers) (pp. 15-20). https://aclanthology.org/N18-2003/

• Week 10 (October 31-): Bias 2

  • October 31: Class-led discussion on Bias 1 - Happy Halloween! 🎃

  • October 31: Fine-tuning paper review due

  • November 2: Instructor-guided discussion on debiasing - Discussion due November 1 at 10pm

    • Cheng, P., Hao, W., Yuan, S., Si, S., & Carin, L. (2020, September). FairFil: Contrastive Neural Debiasing Method for Pretrained Text Encoders. In International Conference on Learning Representations. https://openreview.net/forum?id=N6JECD-PI5w

    • Liang, S., Dufter, P., & Schütze, H. (2020, December). Monolingual and multilingual reduction of gender bias in contextualized representations. In Proceedings of the 28th International Conference on Computational Linguistics (pp. 5082-5093). https://aclanthology.org/2020.coling-main.446/

    • Prost, F., Thain, N., & Bolukbasi, T. (2019). Debiasing embeddings for reduced gender bias in text classification. arXiv preprint arXiv:1908.02810. https://aclanthology.org/W19-3810/

• Week 11 (November 7-): Longer spans

  • November 7: Class-led discussion on Bias 2

  • November 7: Bias 1 paper review due

  • November 9: Instructor-guided discussion on longer spans - Discussion due November 8 at 10pm

    • Zhu, Z., Pan, C., Abdalla, M., & Rudzicz, F. (2020, November). Examining the rhetorical capacities of neural language models. In Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP (pp. 16-32). https://aclanthology.org/2020.blackboxnlp-1.3/

    • Kim, T., Choi, J., Edmiston, D., & Lee, S. G. (2019, September). Are Pre-trained Language Models Aware of Phrases? Simple but Strong Baselines for Grammar Induction. In International Conference on Learning Representations. https://openreview.net/forum?id=H1xPR3NtPB&amp=1

    • Holtzman, A., Buys, J., Du, L., Forbes, M., & Choi, Y. (2019). The curious case of neural text degeneration. arXiv preprint arXiv:1904.09751. https://openreview.net/forum?id=rygGQyrFvH

• Week 12 (November 14-): Contextual representations

  • November 14: Class-led discussion on Longer spans

  • November 14: Bias 2 paper review due

  • November 16: Instructor-guided discussion on contextual word representations - Discussion due November 15 at 10pm

    • Mengjie Zhao, Philipp Dufter, Yadollah Yaghoobzadeh, and Hinrich Schütze. 2020. Quantifying the Contextualization of Word Representations with Semantic Class Probing. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 1219–1234, Online. Association for Computational Linguistics. https://aclanthology.org/2020.findings-emnlp.109

    • Ethayarajh, K. (2019, November). How Contextual are Contextualized Word Representations? Comparing the Geometry of BERT, ELMo, and GPT-2 Embeddings. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) (pp. 55-65). https://aclanthology.org/D19-1006/ - Vishal Rajasekar

• Week 13 (November 21 -): Contextual representations cont.

  • November 21: Student-led discussion of Contextual representations

    • Vishal Rajasekar

  • November 21: Contextual Representations 1 paper review due

  • November 23: Fall break! No class!

• Week 14 (November 28-): Geometry

  • November 28: Instructor-guided discussion on metrics for interpretability

    • Marvin Kaster, Wei Zhao, and Steffen Eger. 2021. Global Explainability of BERT-Based Evaluation Metrics by Disentangling along Linguistic Factors. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 8912–8925, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics. https://aclanthology.org/2021.emnlp-main.701/

    • Ferrando, J., Gállego, G. I., & Costa-jussà, M. R. (2022). Measuring the Mixing of Contextual Information in the Transformer. arXiv preprint arXiv:2203.04212. https://arxiv.org/abs/2203.04212

  • November 28: Contextual Representations 2 paper review due

  • November 30: Instructor-guided discussion on geometric properties of word vectors - Discussion due November 29 at 10pm

    • Chen, B., Fu, Y., Xu, G., Xie, P., Tan, C., Chen, M., & Jing, L. (2020, September). Probing BERT in Hyperbolic Spaces. In International Conference on Learning Representations. https://openreview.net/forum?id=17VnwXYZyhH

    • William Timkey and Marten van Schijndel. 2021. All Bark and No Bite: Rogue Dimensions in Transformer Language Models Obscure Representational Quality. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 4527–4546, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics. https://aclanthology.org/2021.emnlp-main.372/

    • Zhou, K., Ethayarajh, K., Card, D., & Jurafsky, D. (2022, May). Problems with Cosine as a Measure of Embedding Similarity for High Frequency Words. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) (pp. 401-423). https://aclanthology.org/2022.acl-short.45/

    • Chang, T. A., Tu, Z., & Bergen, B. K. (2022). The Geometry of Multilingual Language Model Representations. arXiv preprint arXiv:2205.10964. https://arxiv.org/abs/2205.10964

• Week 15 (December 5-): Wrap-up

  • December 5: Final project presentations

  • December 7: Final project presentations

Paper presentation and discussion guidelines

• 60%: Presentation quality
  • Presentation is of sufficient length (~30 minutes of content or approximately 15-20 slides)
  • Clearly describes the goals of the paper
  • Describes the linguistic phenomena under question
  • Summarizes each of the experiments conducted in the paper and their results
  • Clearly states significance of the work with respect to the course questions

• 40%: Discussion quality
  • Highlights any uncertainty about linguistic phenomena in paper
  • Raises points for discussion from course discussion page
  • Raises additional points from own reading of the paper

Final project guidelines

A final project will help determine your mastery of the course material and refine your ability to initiate, design, analyze, and summarize unique research questions. The general topic of your project should be broadly related to the interpretability and probing literatures discussed in class. The work you do for your final project should be your own and not plagiarized or purely replicating work completed elsewhere (e.g., data science blogs) and must come with an associated thoughtful literature review detailing prior work and scientific motivation. You may attempt to replicate the results or reconstruct the models from published ACL proceedings paper on the basis of that paper alone.

Students who wish to complete a capstone project via 667 should note that the thoroughness of the literature review and a fair, in-depth analysis of the results is critical for the project completion and it is imperative that you meet with the instructor or update them regularly, beginning early in the course. If you intend to complete a capstone project, all relevant paperwork must be completed on time following the CSE or LIN department guidelines and you must tell the instructor the first week of class if it is your intention to complete a capstone project.

• Please use the ACL 2023 $LaTeX$ template from the ACL Github repository: https://github.com/acl-org/acl-style-files

• Papers will be allowed unlimited pages for references

• Please follow the university guidelines for plagiarism:

Accessibility Services and Student Resources:

If you have a disability and may require some type of instructional and/or examination accommodation, please inform me early in the semester so that we can coordinate the accommodations you may need.  If you have not already done so, please contact the Office of Accessibility Services (formerly the Office of Disability Services) University at Buffalo, 60 Capen Hall, Buffalo, NY 14260-1632; email: stu-accessibility@buffalo.edu Phone: 716-645-2608 (voice); 716-645-2616 (TTY); Fax: 716-645-3116; and on the web at http://www.buffalo.edu/studentlife/who-we-are/departments/accessibility.html. All information and documentation is confidential.

The University at Buffalo and the Graduate School of Education are committed to ensuring equal opportunity for persons with special needs to participate in and benefit from all of its programs, services and activities.

Academic Integrity:

Academic integrity is critical to the learning process. It is your responsibility as a student to complete your work in an honest fashion, upholding the expectations your individual instructors have for you in this regard. The ultimate goal is to ensure that you learn the content in your courses in accordance with UB’s academic integrity principles, regardless of whether instruction is in-person or remote. Thank you for upholding your own personal integrity and ensuring UB’s tradition of academic excellence.

It is expected that you will behave in an honorable and respectful way as you learn and share ideas. Therefore, recycled papers, work submitted to other courses, and major assistance in preparation of assignments without identifying and acknowledging such assistance are not acceptable. All work for this class must be original for this class. Please be familiar with the University and the School policies regarding plagiarism. Read the Academic Integrity Policy and Procedure for more information. Visit The Graduate School Policies & Procedures page (http://grad.buffalo.edu/succeed/current-students/policy-library.html) for the latest information.

Course Evaluations:

You will have two opportunities to provide anonymous feedback about the course. In the middle of the semester, I will send you a brief questionnaire asking about what activities are contributing to your learning and what might be done to improve your learning. At the conclusion of the semester you will receive an email reminder requesting your participation in the Course Evaluation process. Please provide your honest feedback; it is important to the improvement and development of this course. Feedback received is anonymous and I do not receive copies of the Evaluations until after grades have been submitted for the semester.

Counseling Services:

As a student you may experience a range of issues that can cause barriers to learning or reduce your ability to participate in daily activities. These might include strained relationships, anxiety, high levels of stress, alcohol/drug problems, feeling down, health concerns, or unwanted sexual experiences. Counseling, Health Services and Health Promotion are here to help with these or other issues you may experience. You can learn more about these program and services by contacting:

Counseling Services

120 Richmond Quad (North Campus), 716-645-2720

202 Michael Hall (South Campus), 716-829-5900

https://www.buffalo.edu/studentlife/who-we-are/departments/counseling.html

Health Services

Michael Hall (South Campus), 716-829-3316

https://www.buffalo.edu/studentlife/who-we-are/departments/health.html

Office of Health Promotion

114 Student Union (North Campus), 716-645-2837

https://www.buffalo.edu/studentlife/who-we-are/departments/health-promotion.html

Sexual Harassment/Violence:

UB is committed to providing a safe learning environment free of all forms of discrimination and sexual harassment, including sexual assault, domestic and dating violence and stalking. If you have experienced gender-based violence (intimate partner violence, attempted or completed sexual assault, harassment, coercion, stalking, etc.), UB has resources to help. This includes academic accommodations, health and counseling services, housing accommodations, helping with legal protective orders, and assistance with reporting the incident to police or other UB officials if you so choose. Please contact UB’s Title IX Coordinator at 716-645-2266 for more information. For confidential assistance, you may also contact a Crisis Service Campus Advocate at 716-796-4399.

Please be aware UB faculty are mandated to report violence or harassment on the basis of sex or gender. This means that if you tell me about a situation, I will need to report it to the Office of Equity, Diversity and Inclusion. You will still have options about how the situation will be handled, including whether or not you wish to pursue a formal complaint. Please know that if you not wish to have UB proceed with an investigation, your request will be honored unless UB’s failure to act does not adequately mitigate the risk of harm to you or other members of the university community. You also have the option of speaking with trained counselors who can maintain confidentiality. UB’s Options for Confidentiality Disclosing Sexual Violence provides a full explanation of the resources available, as well as contact information. You may call UB’s Office of Equity, Diversity and Inclusion at 716-645-2266 for more information, and you have the option of calling that office anonymously if you would prefer not to disclose your identity.

Technology Recommendations

To effectively participate in this course, regardless of mode of instruction, the university recommends you have access to a Windows or Mac computer with webcam and broadband. Your best opportunity for success in the blended UB course delivery environment (in-person, hybrid and remote) will require these minimum capabilities.

Public health compliance in a classroom setting

UB student Behavioral Requirements in all Campus Public Spaces include:

1. Students are required to obtain and wear a high-quality, tight-fitting, high-filtration mask when aboard a UB bus or shuttle or in a clinical health care setting in accordance with current health and safety guidelines. Masks indoors and in other public campus settings are optional.

2. Students who are regularly on campus and not fully vaccinated are required to participate in surveillance testing.

3. Students are required to abide by New York State, federal and Center for Disease Control and Prevention (CDC) travel restrictions and precautionary quarantines.

4. Students are required to stay home if they are sick.

5. Students are required to follow campus and public health directives for isolation or quarantine.

6. Should a student need to miss class due to illness, isolation or quarantine, they are required to notify their faculty to make arrangements to make up missed work.

7. Living on campus is a privilege that comes with additional requirements. Residential students are required to follow specific Campus Living rules as outlined in the Campus Living Housing Agreement, the Guide to Campus Living and any posted signage.

8. Students dining at on-campus facilities are expected to follow posted information on any additional requirements specific to the dining environment.

9. Students are responsible for following any additional directives in settings such as labs, clinical environments etc.