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: email@example.com Office Location: 614 Baldy Hall Office Hours: On Zoom by appointment
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).
|Course Learning Outcome||Instructional Methods||Assessment Methods|
|Obtain familiarity with probing methods to test for the presence of linguistic structure in neural language model representations||Classroom presentation; leading class discussion; course reading posts; classroom participation||Completion rubrics|
|Understand the effects of including linguistic features or metadata as part of neural language model feature engineering; better understand types of probes and fine-tuning methods||Course readings and classroom participation||Forum participation on Blackboard/UBLearns|
|Obtain fluency in communication of scientific results and discussion of scientific topics||Classroom presentation; leading class discussion; final project||Presentation rubric; forum participation on Blackboard/UBLearns|
|Obtain competency in the design and completion of a research project||Final project||Completion rubrics|
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.
|1||Wednesday||Topic 1: Instructor-guided discussion|
|2||Monday||Topic 1: Student-led discussion|
|2||Wednesday||Topic 2: Instructor-guided discussion|
|3||Monday||Topic 2: Student-led discussion|
|3||Wednesday||Topic 3: Instructor-guided discussion (Topic 3)|
|Topic 1: Paper reviews|
|4||Monday||Topic 3: Student-led discussion|
|4||Wednesday||Topic 4: Instructor-guided discussion|
|Paper reviews (Topic 2)|
|Weight (percent of overall grade)||Assignment|
|40%||Weekly reviews posted to UBLearns|
|20%||Student-led presentation & discussion|
|20%||Final project paper|
|10%||Final project presentation|
|10%||Participation in class and monthly self-assessments|
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
Week 1 (August 29-): Interpretability and probes
- 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
- 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
- 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: 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: 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: 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: 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: 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: 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: 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&=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: 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: 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: 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 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:
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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: firstname.lastname@example.org 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.
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