12 citations · 18 across the 9 of their papers we have counts for
15 papers · 1 filter
Russian SuperGLUE 1.1: Revising the Lessons not Learned by Russian NLP models
Alena Fenogenova, Maria Tikhonova, Vladislav Mikhailov +6
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 langua…
Razmecheno: Named Entity Recognition from Digital Archive of Diaries "Prozhito"
Timofey Atnashev, Veronika Ganeeva, Roman Kazakov +5
The vast majority of existing datasets for Named Entity Recognition (NER) are built primarily on news, research papers and Wikipedia with a few exceptions, created from historical…
NEREL: A Russian Dataset with Nested Named Entities, Relations and Events
Natalia Loukachevitch, Ekaterina Artemova, Tatiana Batura +6
In this paper, we present NEREL, a Russian dataset for named entity recognition and relation extraction. NEREL is significantly larger than existing Russian datasets: to date it co…
A Single Example Can Improve Zero-Shot Data Generation
Pavel Burnyshev, Valentin Malykh, Andrey Bout +2
Sub-tasks of intent classification, such as robustness to distribution shift, adaptation to specific user groups and personalization, out-of-domain detection, require extensive and…
A Differentiable Language Model Adversarial Attack on Text Classifiers
Ivan Fursov, Alexey Zaytsev, Pavel Burnyshev +5
Robustness of huge Transformer-based models for natural language processing is an important issue due to their capabilities and wide adoption. One way to understand and improve rob…
Morph Call: Probing Morphosyntactic Content of Multilingual Transformers
Vladislav Mikhailov, Oleg Serikov, Ekaterina Artemova
The outstanding performance of transformer-based language models on a great variety of NLP and NLU tasks has stimulated interest in exploring their inner workings. Recent research…