Anna Rogers (Gladkova)

Contact GScholar CV GitHub LinkedIn Blog

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.

Kovaleva, O., Romanov, A., Rogers, A., & Rumshisky, A. (2019). Revealing the Dark Secrets of BERT. Accepted for EMNLP-IJCNLP 2019.
Rogers, A., Kovaleva, O., & Rumshisky, A. (2019). Calls to Action on Social Media: Potential for Censorship and Social Impact. Accepted for EMNLP-IJCNLP 2019 Second Workshop on Natural Language Processing for Internet Freedom.
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).
Rogers, A., Drozd, A., Rumshisky, A., & Goldberg, Y. (2019). Proceedings of the 3rd Workshop on Evaluating Vector Space Representations for NLP.

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).
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).
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).
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).
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).
Rogers, A. (2017). Multilingual computational lexicography: frame semantics meets distributional semantics (Ph.D. dissertation). University of Tokyo, Tokyo.

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.
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.
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.
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.

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).
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.
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


English, Japanese, French, Ukrainian, Russian

How to test machine reading comprehension?
28 October 2019: Rigorous Evaluation of AI Systems Workshop (co-located with HCOMP) (USA).

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:

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


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


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

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)


NAACL, COLING, *SEM, RepEval, Language Resources and Evaluation


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)

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)