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.
no code implementations • WS 2018 • Valentin Malykh, Varvara Logacheva, Taras Khakhulin
We suggest a new language-independent architecture of robust word vectors (RoVe).
no code implementations • 28 Nov 2018 • Elena Tutubalina, Zulfat Miftahutdinov, Sergey Nikolenko, Valentin Malykh
In this work, we consider the medical concept normalization problem, i. e., the problem of mapping a disease mention in free-form text to a concept in a controlled vocabulary, usually to the standard thesaurus in the Unified Medical Language System (UMLS).
no code implementations • 23 Jan 2019 • Daniil Gavrilov, Pavel Kalaidin, Valentin Malykh
Headline generation is a special type of text summarization task.
no code implementations • 23 Jan 2019 • Sergey I. Nikolenko, Elena Tutubalina, Valentin Malykh, Ilya Shenbin, Anton Alekseev
We propose a novel end-to-end Aspect-based Rating Prediction model (AspeRa) that estimates user rating based on review texts for the items and at the same time discovers coherent aspects of reviews that can be used to explain predictions or profile users.
2 code implementations • 31 Jan 2019 • Emily Dinan, Varvara Logacheva, Valentin Malykh, Alexander Miller, Kurt Shuster, Jack Urbanek, Douwe Kiela, Arthur Szlam, Iulian Serban, Ryan Lowe, Shrimai Prabhumoye, Alan W. black, Alexander Rudnicky, Jason Williams, Joelle Pineau, Mikhail Burtsev, Jason Weston
We describe the setting and results of the ConvAI2 NeurIPS competition that aims to further the state-of-the-art in open-domain chatbots.
2 code implementations • ACL 2019 • Valentin Malykh
There are a lot of noise texts surrounding a person in modern life.
no code implementations • WS 2019 • Elena Tutubalina, Valentin Malykh, Sergey Nikolenko, Anton Alekseev, Ilya Shenbin
We propose a novel Aspect-based Rating Prediction model (AspeRa) that estimates user rating based on review texts for the items.
3 code implementations • 24 Dec 2019 • Ilya Shenbin, Anton Alekseev, Elena Tutubalina, Valentin Malykh, Sergey I. Nikolenko
Recent research has shown the advantages of using autoencoders based on deep neural networks for collaborative filtering.
Ranked #2 on Recommendation Systems on Netflix
1 code implementation • 7 Apr 2020 • Elena Tutubalina, Ilseyar Alimova, Zulfat Miftahutdinov, Andrey Sakhovskiy, Valentin Malykh, Sergey Nikolenko
For the sentence classification task, our model achieves the macro F1 score of 68. 82% gaining 7. 47% over the score of BERT model trained on Russian data.
1 code implementation • LREC 2020 • Tatiana Shavrina, Anton Emelyanov, Alena Fenogenova, Vadim Fomin, Vladislav Mikhailov, Andrey Evlampiev, Valentin Malykh, Vladimir Larin, Alex Natekin, Aleks Vatulin, R, Peter Romov, Daniil Anastasiev, Nikolai Zinov, Andrey Chertok
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).
no code implementations • 17 Jun 2020 • Anton Alekseev, Elena Tutubalina, Valentin Malykh, Sergey Nikolenko
Deep learning architectures based on self-attention have recently achieved and surpassed state of the art results in the task of unsupervised aspect extraction and topic modeling.
2 code implementations • EMNLP 2020 • Tatiana Shavrina, Alena Fenogenova, Anton Emelyanov, Denis Shevelev, Ekaterina Artemova, Valentin Malykh, Vladislav Mikhailov, Maria Tikhonova, Andrey Chertok, Andrey Evlampiev
In this paper, we introduce an advanced Russian general language understanding evaluation benchmark -- RussianGLUE.
Ranked #1 on Word Sense Disambiguation on RUSSE
no code implementations • COLING 2020 • Valentin Malykh, Konstantin Chernis, Ekaterina Artemova, Irina Piontkovskaya
The existing dialogue summarization corpora are significantly extractive.
3 code implementations • 29 Apr 2021 • Valentin Malykh, Alexander Kukushkin, Ekaterina Artemova, Vladislav Mikhailov, Maria Tikhonova, Tatiana Shavrina
The new generation of pre-trained NLP models push the SOTA to the new limits, but at the cost of computational resources, to the point that their use in real production environments is often prohibitively expensive.
no code implementations • NeurIPS 2021 • Tatiana Shavrina, Valentin Malykh
Proper model ranking and comparison with a human level is an essential requirement for every benchmark to be a reliable measurement of the model quality.
no code implementations • 16 Aug 2021 • Pavel Burnyshev, Valentin Malykh, Andrey Bout, Ekaterina Artemova, Irina Piontkovskaya
In the zero-shot approach, the model is trained to generate utterances from seen intents and is further used to generate utterances for intents unseen during training.
no code implementations • 15 Feb 2022 • Alena Fenogenova, Maria Tikhonova, Vladislav Mikhailov, Tatiana Shavrina, Anton Emelyanov, Denis Shevelev, Alexandr Kukushkin, Valentin Malykh, Ekaterina Artemova
In the last year, new neural architectures and multilingual pre-trained models have been released for Russian, which led to performance evaluation problems across a range of language understanding tasks.
1 code implementation • 23 Apr 2022 • Pavel Tikhonov, Valentin Malykh
Cross-lingual summarization (CLS) is the task to produce a summary in one particular language for a source document in a different language.
Abstractive Text Summarization Cross-Lingual Abstractive Summarization
no code implementations • 22 Jun 2022 • Dmitry Lamanov, Pavel Burnyshev, Ekaterina Artemova, Valentin Malykh, Andrey Bout, Irina Piontkovskaya
We outperform previous state-of-the-art f1-measure by up to 16\% for unseen intents, using intent labels and user utterances and without accessing external sources (such as knowledge bases).
1 code implementation • 24 Jun 2022 • Michael Vasilkovsky, Anton Alekseev, Valentin Malykh, Ilya Shenbin, Elena Tutubalina, Dmitriy Salikhov, Mikhail Stepnov, Andrey Chertok, Sergey Nikolenko
Our model sets the new state of the art performance of 67. 7% F1 on CaRB evaluated as OIE2016 while being 3. 35x faster at inference than previous state of the art.
Ranked #1 on Open Information Extraction on LSOIE
no code implementations • 19 May 2023 • Nikita Sorokin, Dmitry Abulkhanov, Sergey Nikolenko, Valentin Malykh
We consider the clone detection and information retrieval problems for source code, well-known tasks important for any programming language.
no code implementations • 19 May 2023 • Ivan Sedykh, Dmitry Abulkhanov, Nikita Sorokin, Sergey Nikolenko, Valentin Malykh
Code search is an important task that has seen many developments in recent years.
no code implementations • 3 Oct 2023 • Mikhail Salnikov, Hai Le, Prateek Rajput, Irina Nikishina, Pavel Braslavski, Valentin Malykh, Alexander Panchenko
Recently, it has been shown that the incorporation of structured knowledge into Large Language Models significantly improves the results for a variety of NLP tasks.
no code implementations • 10 Oct 2023 • Mikhail Salnikov, Maria Lysyuk, Pavel Braslavski, Anton Razzhigaev, Valentin Malykh, Alexander Panchenko
Pre-trained Text-to-Text Language Models (LMs), such as T5 or BART yield promising results in the Knowledge Graph Question Answering (KGQA) task.
no code implementations • NAACL 2022 • Nikita Sorokin, Dmitry Abulkhanov, Irina Piontkovskaya, Valentin Malykh
Cross-lingual question answering is a thriving field in the modern world, helping people to search information on the web more efficiently.
no code implementations • loresmt (AACL) 2020 • Atul Kr. Ojha, Valentin Malykh, Alina Karakanta, Chao-Hong Liu
This paper presents the findings of the LoResMT 2020 Shared Task on zero-shot translation for low resource languages.
no code implementations • RANLP 2021 • Pavel Burnyshev, Andrey Bout, Valentin Malykh, Irina Piontkovskaya
Natural language understanding is an important task in modern dialogue systems.
no code implementations • RANLP 2021 • Artur Ilichev, Nikita Sorokin, Irina Piontkovskaya, Valentin Malykh
The language models nowadays are in the center of natural language processing progress.
no code implementations • INLG (ACL) 2021 • Pavel Burnyshev, Valentin Malykh, Andrey Bout, Ekaterina Artemova, Irina Piontkovskaya
We explore two approaches to the generation of task-oriented utterances: in the zero-shot approach, the model is trained to generate utterances from seen intents and is further used to generate utterances for intents unseen during training.