2 code implementations • 16 Feb 2024 • Yuri Kuratov, Aydar Bulatov, Petr Anokhin, Dmitry Sorokin, Artyom Sorokin, Mikhail Burtsev
This paper addresses the challenge of processing long documents using generative transformer models.
1 code implementation • 2 Nov 2023 • Alla Chepurova, Aydar Bulatov, Yuri Kuratov, Mikhail Burtsev
In this study, we propose to include node neighborhoods as additional information to improve KGC methods based on language models.
3 code implementations • 19 Apr 2023 • Aydar Bulatov, Yuri Kuratov, Yermek Kapushev, Mikhail S. Burtsev
A major limitation for the broader scope of problems solvable by transformers is the quadratic scaling of computational complexity with input size.
3 code implementations • 14 Jul 2022 • Aydar Bulatov, Yuri Kuratov, Mikhail S. Burtsev
We implement a memory mechanism with no changes to Transformer model by adding special memory tokens to the input or output sequence.
1 code implementation • 4 May 2022 • Alina Kolesnikova, Yuri Kuratov, Vasily Konovalov, Mikhail Burtsev
We propose two simple yet effective alignment techniques to make knowledge distillation to the students with reduced vocabulary.
1 code implementation • 20 Jun 2020 • Mikhail S. Burtsev, Yuri Kuratov, Anton Peganov, Grigory V. Sapunov
Adding trainable memory to selectively store local as well as global representations of a sequence is a promising direction to improve the Transformer model.
no code implementations • 5 Feb 2020 • Pavel Gulyaev, Eugenia Elistratova, Vasily Konovalov, Yuri Kuratov, Leonid Pugachev, Mikhail Burtsev
The organizers introduced the Schema-Guided Dialogue (SGD) dataset with multi-domain conversations and released a zero-shot dialogue state tracking model.
1 code implementation • WS 2019 • Mikhail Arkhipov, Maria Trofimova, Yuri Kuratov, Alexey Sorokin
Our paper addresses the problem of multilingual named entity recognition on the material of 4 languages: Russian, Bulgarian, Czech and Polish.
Multilingual Named Entity Recognition named-entity-recognition +3
2 code implementations • 17 May 2019 • Yuri Kuratov, Mikhail Arkhipov
This work shows that transfer learning from a multilingual model to monolingual model results in significant growth of performance on such tasks as reading comprehension, paraphrase detection, and sentiment analysis.
Ranked #1 on Question Answering on SQuAD1.1 (Hardware Burden metric)
1 code implementation • Conference: Proceedings of the 7th Workshop on Balto-Slavic Natural Language Processing 2019 • Mikhail Arkhipov, Maria Trofimova, Yuri Kuratov, Alexey Sorokin
Our paper addresses the problem of multilingual named entity recognition on the material of 4 languages: Russian, Bulgarian, Czech and Polish.
Multilingual Named Entity Recognition named-entity-recognition +3
no code implementations • ACL 2018 • Mikhail Burtsev, Alex Seliverstov, er, Rafael Airapetyan, Mikhail Arkhipov, Dilyara Baymurzina, Nickolay Bushkov, Olga Gureenkova, Taras Khakhulin, Yuri Kuratov, Denis Kuznetsov, Alexey Litinsky, Varvara Logacheva, Alexey Lymar, Valentin Malykh, Maxim Petrov, Vadim Polulyakh, Leonid Pugachev, Alexey Sorokin, Maria Vikhreva, Marat Zaynutdinov
It supports modular as well as end-to-end approaches to implementation of conversational agents.