Papers by Tatiana Shavrina
Humans Keep It One Hundred: an Overview of AI Journey (2020.lrec-1)
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Tatiana Shavrina, Anton Emelyanov, Alena Fenogenova, Vadim Fomin, Vladislav Mikhailov, Andrey Evlampiev, Valentin Malykh, Vladimir Larin, Alex Natekin, Aleksandr Vatulin, Peter Romov, Daniil Anastasiev, Nikolai Zinov, Andrey Chertok
| Challenge: | Artificial General Intelligence (AGI) is showing growing performance in numerous applications - beating human performance in Chess and Go, using knowledge bases and text sources to answer questions and even pass human examination. |
| Approach: | They propose to use knowledge bases and text sources to answer questions to improve AI performance on knowledge bases, reasoning and text generation. |
| Outcome: | The proposed AI Journey system passed the final native language exam in Russian with a high score of 69%, with 68% being an average human result. |
A Family of Pretrained Transformer Language Models for Russian (2024.lrec-main)
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Dmitry Zmitrovich, Aleksandr Abramov, Andrey Kalmykov, Vitaly Kadulin, Maria Tikhonova, Ekaterina Taktasheva, Danil Astafurov, Mark Baushenko, Artem Snegirev, Tatiana Shavrina, Sergei S. Markov, Vladislav Mikhailov, Alena Fenogenova
| Challenge: | Developing Transformer language models for the Russian language has received little attention . most of these LMs are developed for English, which imposes substantial constraints on the potential of the language technologies. |
| Approach: | They propose to release 13 Russian Transformer language models that span three languages . they aim to broaden the scope of NLP research directions and develop industrial solutions for the Russian language. |
| Outcome: | The proposed models are based on Russian language datasets and benchmarks. |
Attention Understands Semantic Relations (2022.lrec-1)
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| Challenge: | Present-day monopoly of foundation language models in most tasks forces researchers and practitioners to rely on popular large models without genuinely understanding the models' behaviour. |
| Approach: | They propose a probing pipeline to study the representedness of semantic relations in transformer language models and propose 'attention mechanisms' that focus on syntactic relational information and semantic one. |
| Outcome: | The proposed pipeline shows that attention scores are expressive as output activations on this task, despite their lesser ability to represent surface cues. |
TAPE: Assessing Few-shot Russian Language Understanding (2022.findings-emnlp)
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Ekaterina Taktasheva, Tatiana Shavrina, Alena Fenogenova, Denis Shevelev, Nadezhda Katricheva, Maria Tikhonova, Albina Akhmetgareeva, Oleg Zinkevich, Anastasiia Bashmakova, Svetlana Iordanskaia, Alena Spiridonova, Valentina Kurenshchikova, Ekaterina Artemova, Vladislav Mikhailov
| Challenge: | Recent advances in zero-shot and few-shot learning have shown promise for a scope of research and practical purposes, but lacks standardized evaluation suites for non-English languages. |
| Approach: | They propose a novel benchmark that includes six more complex NLU tasks for Russian, covering multi-hop reasoning, ethical concepts, logic and commonsense knowledge. |
| Outcome: | The proposed benchmark includes six more complex NLU tasks for Russian, covering multi-hop reasoning, ethical concepts, logic and commonsense knowledge. |
Vote’n’Rank: Revision of Benchmarking with Social Choice Theory (2023.eacl-main)
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Mark Rofin, Vladislav Mikhailov, Mikhail Florinsky, Andrey Kravchenko, Tatiana Shavrina, Elena Tutubalina, Daniel Karabekyan, Ekaterina Artemova
| Challenge: | ML benchmarks have been criticized for their construct validity, fragility of the design and task choices. |
| Approach: | They propose a framework for ranking systems in multi-task benchmarks under the principles of the social choice theory and propose 'vote'n'rank' procedures are more robust than the mean average while being able to handle missing performance scores and determine conditions under which the system becomes the winner. |
| Outcome: | The proposed framework can be utilised to draw new insights on benchmarking in several ML sub-fields and identify the best-performing systems in research and development case studies. |
RussianSuperGLUE: A Russian Language Understanding Evaluation Benchmark (2020.emnlp-main)
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Tatiana Shavrina, Alena Fenogenova, Emelyanov Anton, Denis Shevelev, Ekaterina Artemova, Valentin Malykh, Vladislav Mikhailov, Maria Tikhonova, Andrey Chertok, Andrey Evlampiev
| Challenge: | Modern scientific methodology is beginning to explore universal transformers as an independent object of study. |
| Approach: | They propose a Russian general language understanding evaluation benchmark - Russian SuperGLUE . they provide a benchmark of nine tasks, human level evaluation and a leaderboard for the Russian language . |
| Outcome: | The proposed benchmark provides nine tasks for the Russian language and human level evaluation and leaderboard of transformer models. |