Andrey Evlampiev


2020

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Humans Keep It One Hundred: an Overview of AI Journey
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
Proceedings of the Twelfth Language Resources and Evaluation Conference

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 (SQuAD) and even pass human examination (Aristo project). In this paper, we describe the results of AI Journey, a competition of AI-systems aimed to improve AI performance on knowledge bases, reasoning and text generation. Competing systems pass the final native language exam (in Russian), including versatile grammar tasks (test and open questions) and an essay, achieving a high score of 69%, with 68% being an average human result. During the competition, a baseline for the task and essay parts was proposed, and 80+ systems were submitted, showing different approaches to task understanding and reasoning. All the data and solutions can be found on github https://github.com/sberbank-ai/combined_solution_aij2019

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RussianSuperGLUE: A Russian Language Understanding Evaluation Benchmark
Tatiana Shavrina | Alena Fenogenova | Emelyanov Anton | Denis Shevelev | Ekaterina Artemova | Valentin Malykh | Vladislav Mikhailov | Maria Tikhonova | Andrey Chertok | Andrey Evlampiev
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

In this paper, we introduce an advanced Russian general language understanding evaluation benchmark – Russian SuperGLUE. Recent advances in the field of universal language models and transformers require the development of a methodology for their broad diagnostics and testing for general intellectual skills - detection of natural language inference, commonsense reasoning, ability to perform simple logical operations regardless of text subject or lexicon. For the first time, a benchmark of nine tasks, collected and organized analogically to the SuperGLUE methodology, was developed from scratch for the Russian language. We also provide baselines, human level evaluation, open-source framework for evaluating models, and an overall leaderboard of transformer models for the Russian language. Besides, we present the first results of comparing multilingual models in the translated diagnostic test set and offer the first steps to further expanding or assessing State-of-the-art models independently of language.