Anna Rogers (Gladkova)

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I am a post-doctoral associate in the Computer Science Department at Text Machine lab, University of Massachusetts (Lowell). I work at the intersection of linguistics, natural language processing, and machine learning. I hold a Ph.D. degree from the Department of Language and Information Sciences at the University of Tokyo (Japan).

My projects span distributional meaning representations and their evaluation, question answering, temporal reasoning, and computational social science.

2020
Rogers, A., Kovaleva, O., Downey, M., & Rumshisky, A. (2020). Getting Closer to AI Complete Question Answering: A Set of Prerequisite Real Tasks. Accepted for AAAI 2020.
2019
Kovaleva, O., Romanov, A., Rogers, A., & Rumshisky, A. (2019). Revealing the Dark Secrets of BERT. In Proceedings of EMNLP-IJCNLP 2019 (pp.) https://www.aclweb.org/anthology/D19-1445/.
Rogers, A., Kovaleva, O., & Rumshisky, A. (2019). Calls to Action on Social Media: Potential for Censorship and Social Impact. In Proceedings of Second Workshop on Natural Language Processing for Internet Freedom (co-located with EMNLP-IJCNLP 2019) (pp.) https://www.aclweb.org/anthology/D19-5005/.
Romanov, A., Rumshisky, A., Rogers, A., & Donahue, D. (2019). Adversarial Decomposition of Text Representation. 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) (pp. 815–825). https://aclweb.org/anthology/papers/N/N19/N19-1088/
Rogers, A., Drozd, A., Rumshisky, A., & Goldberg, Y. (2019). Proceedings of the 3rd Workshop on Evaluating Vector Space Representations for NLP. https://www.aclweb.org/anthology/papers/W/W19/W19-2000/

2018
Rogers, A., Hosur Anathakrishna, Sh., & Rumshisky, A. What's in Your Embedding, And How It Predicts Task Performance. In Proceedings of the 27th International Conference on Computational Linguistics (pp. 2690–2703). http://aclweb.org/anthology/C18-1228
Rogers, A., Romanov, A., Rumshisky, A., Volkova, S., Gronas, M., & Gribov, A. RuSentiment: An Enriched Sentiment Analysis Dataset for Social Media in Russian. In Proceedings of the 27th International Conference on Computational Linguistics (pp. 755–763). http://aclweb.org/anthology/C18-1064
Karpinska, M., Li, B., Rogers, A. & Drozd, A. Subcharacter Information in Japanese Embeddings: When Is It Worth It? In Proceedings of the Workshop on the Relevance of Linguistic Structure in Neural Architectures for NLP (pp. 28–37). http://www.aclweb.org/anthology/W18-2905
2017
Rogers, A., Drozd, A., & Li, B. (2017). The (Too Many) Problems of Analogical Reasoning with Word Vectors. In Proceedings of the 6th Joint Conference on Lexical and Computational Semantics (* SEM 2017) (pp. 135–148). http://www.aclweb.org/anthology/S17-1017
Li, B., Liu, T., Zhao, Z., Tang, B., Drozd, A., Rogers, A., & Du, X. (2017). Investigating different syntactic context types and context representations for learning word embeddings. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (pp. 2411–2421). http://www.aclweb.org/anthology/D17-1256
Rogers, A. (2017). Multilingual computational lexicography: frame semantics meets distributional semantics (Ph.D. dissertation). University of Tokyo, Tokyo.

2016
Gladkova, A., Drozd, A., & Matsuoka, S. (2016). Analogy-based detection of morphological and semantic relations with word embeddings: what works and what doesn’t. In Proceedings of the NAACL-HLT SRW (pp. 47–54). San Diego, California, June 12-17, 2016: ACL. https://doi.org/10.18653/v1/N16-2002
Gladkova, A., & Drozd, A. (2016). Intrinsic evaluations of word embeddings: what can we do better? In Proceedings of The 1st Workshop on Evaluating Vector Space Representations for NLP (pp. 36–42). Berlin, Germany: ACL. https://doi.org/10.18653/v1/W16-2507
Drozd, A., Gladkova, A., & Matsuoka, S. (2016). Word embeddings, analogies, and machine learning: beyond king - man + woman = queen. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers (pp. 3519–3530). Osaka, Japan, December 11-17. https://www.aclweb.org/anthology/C/C16/C16-1332.pdf
Santus, E., Gladkova, A., Evert, S., & Lenci, A. (2016). The CogALex-V shared task on the corpus-based identification of semantic relations. In Proceedings of the Workshop on Cognitive Aspects of the Lexicon (pp. 69–79). Osaka, Japan, December 11-17: ACL. http://www.aclweb.org/anthology/W/W16/W16-53.pdf#page=83

2015
Drozd, A., Gladkova, A., & Matsuoka, S. (2015). Discovering aspectual classes of Russian verbs in untagged large corpora. In Proceedings of 2015 IEEE International Conference on Data Science and Data Intensive Systems (DSDIS) (pp. 61–68). https://doi.org/10.1109/DSDIS.2015.30
Drozd, A., Gladkova, A., & Matsuoka, S. (2015). Python, performance, and Natural Language Processing. In Proceedings of the 5th Workshop on Python for High-Performance and Scientific Computing (p. 1:1–1:10). New York, NY, USA: ACM. https://doi.org/10.1145/2835857.2835858
Programming & scripting

Python, JavaScript, Matlab/Octave, Bash;

Machine learning

scikit-learn, PyTorch, TensorFlow

Theoretical frameworks

Distributional semantics, frame semantics, sociolinguistics, pragmatics, discourse analysis, diachronic analysis of languages

Languages

English, Japanese, French, Ukrainian, Russian

How to test machine reading comprehension?
8 Feb 2020: Evaluating Evaluation of AI Systems (Workshop co-located with AAAI 2020, URL) (New York, USA).

Towards AI Complete Question Answering: Combining Text-based, Unanswerable and World Knowledge Questions
11 December 2019: Allen Institute for Aritficial Intelligence (Seattle, USA).

Text Representations Learning and Compositional Semantics (ACML 2019 tutorial, URL)
November 17 2019: Nagoya, Japan


The dark secrets of BERT
11 November 2019: RIKEN Center for Computational Science (Tokyo, Japan).

Word embeddings: 6 years later.
22 May 2019: UMass Amherst (USA). [SLIDES]

What's in your embedding, and how it predicts task performance.
27 September 2018: UMass Amherst (USA). [SLIDES] [VIDEO]
A version of this talk was also presented on August 30 2018 at IT University of Copenhagen (Denmark).


Distributional compositional semantics in the age of word embeddings.
7 May 2018: Tutorial T4 at LREC 2018, Miyazaki, Japan.
Tutorial website: http://text-machine.cs.uml.edu/lrec2018_t4/index.html


Detecting linguistic relations with analogies: what works and what doesn't.
July 15 2016: Google Tokyo seminar, Tokyo, Japan. [SLIDES]

Workshops

RepEval 2019: The Third Workshop on Evaluating Vector Space Representations for NLP (URL)
June 6 2019: Minneapolis, USA (co-located with NAACL 2019)


Insights 2020: The First Workshop on Insights from Negative Results in NLP (URL)
November 11-12 2020: Punta Cana, Dominican Republic (co-located with EMNLP 2020)


Tutorials

T4 LREC 2018 tutorial: Distributional compositional semantics in the age of word embeddings: tasks, resources and methodology (URL)
May 7, 2018: Miyazaki, Japan (LREC 2018)


ACML 2019 tutorial: Text Representations Learning and Compositional Semantics (URL)
November 17, 2019: Nagoya, Japan


COLING 2020 tutorial: A guide to the dataset explosion in QA, NLI, and commonsense reasoning (TBA)
September 13-14, 2020: Barcelona, Spain


Shared tasks

CogALex-V Shared Task on the Corpus-Based Identification of Semantic Relations (URL)
December 12, 2016: Osaka, Japan (Cognitive Aspects of the Lexicon Workshop, co-located with COLING 2016)


Reviewing

ACL, NAACL, COLING, *SEM, RepEval, Cognitive Processing


COMP-1005: Introduction to Programming for Data Science (URL)
University of Massachusetts Lowell, Computer Science department, spring 2019


NLP with Python @ ESSLLI: Introduction to NLP with Python (beginner & advanced - a suite of two 1-week courses) (URL)
Riga, Latvia, August 5-16 2019 (European Summer School in Logic, Language and Information 2019)

Media

Hacking Semantics: a blog on computational linguistics, cognition, AI and NLP (URL)


WIRED: Artificial Intelligence Confronts a 'Reproducibility' Crisis, 09.16.2019 (URL)

Tech Xplore: Investigating the self-attention mechanism behind BERT-based architectures, 11.09.2019 (URL)

Quanta, WIRED: Machines Beat Humans on a Reading Test. But Do They Understand?, 17.10.2019 (URL, URL)


Cross-disciplinary lectures

NLP with Python @ ESSLLI 2019: an introductory NLP course aimed primarily at linguists rather than computer scientists (URL, reflections on the course)