10 citations · 17 across the 5 of their papers we have counts for
6 papers
Pillars of Grammatical Error Correction: Comprehensive Inspection Of Contemporary Approaches In The Era of Large Language Models
Kostiantyn Omelianchuk, Andrii Liubonko, Oleksandr Skurzhanskyi +3
In this paper, we carry out experimental research on Grammatical Error Correction, delving into the nuances of single-model systems, comparing the efficiency of ensembling and rank…
Privacy- and Utility-Preserving NLP with Anonymized Data: A case study of Pseudonymization
Oleksandr Yermilov, Vipul Raheja, Artem Chernodub
This work investigates the effectiveness of different pseudonymization techniques, ranging from rule-based substitutions to using pre-trained Large Language Models (LLMs), on a var…
Ensembling and Knowledge Distilling of Large Sequence Taggers for Grammatical Error Correction
Maksym Tarnavskyi, Artem Chernodub, Kostiantyn Omelianchuk
In this paper, we investigate improvements to the GEC sequence tagging architecture with a focus on ensembling of recent cutting-edge Transformer-based encoders in Large configurat…
GECToR -- Grammatical Error Correction: Tag, Not Rewrite
Kostiantyn Omelianchuk, Vitaliy Atrasevych, Artem Chernodub +1
In this paper, we present a simple and efficient GEC sequence tagger using a Transformer encoder. Our system is pre-trained on synthetic data and then fine-tuned in two stages: fir…
Sampling-based Gradient Regularization for Capturing Long-Term Dependencies in Recurrent Neural Networks
Artem Chernodub, Dimitri Nowicki
Vanishing (and exploding) gradients effect is a common problem for recurrent neural networks with nonlinear activation functions which use backpropagation method for calculation of…
Norm-preserving Orthogonal Permutation Linear Unit Activation Functions (OPLU)
Artem Chernodub, Dimitri Nowicki
We propose a novel activation function that implements piece-wise orthogonal non-linear mappings based on permutations. It is straightforward to implement, and very computationally…