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). 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/
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
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.
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
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
scikit-learn, PyTorch, TensorFlow
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: 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]
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
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)