Anna Glazkova


2022

pdf bib
Detecting generated scientific papers using an ensemble of transformer models
Anna Glazkova | Maksim Glazkov
Proceedings of the Third Workshop on Scholarly Document Processing

The paper describes neural models developed for the DAGPap22 shared task hosted at the Third Workshop on Scholarly Document Processing. This shared task targets the automatic detection of generated scientific papers. Our work focuses on comparing different transformer-based models as well as using additional datasets and techniques to deal with imbalanced classes. As a final submission, we utilized an ensemble of SciBERT, RoBERTa, and DeBERTa fine-tuned using random oversampling technique. Our model achieved 99.24% in terms of F1-score. The official evaluation results have put our system at the third place.

2021

pdf bib
MIPT-NSU-UTMN at SemEval-2021 Task 5: Ensembling Learning with Pre-trained Language Models for Toxic Spans Detection
Mikhail Kotyushev | Anna Glazkova | Dmitry Morozov
Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)

This paper describes our system for SemEval-2021 Task 5 on Toxic Spans Detection. We developed ensemble models using BERT-based neural architectures and post-processing to combine tokens into spans. We evaluated several pre-trained language models using various ensemble techniques for toxic span identification and achieved sizable improvements over our baseline fine-tuned BERT models. Finally, our system obtained a F1-score of 67.55% on test data.

2020

pdf bib
UTMN at SemEval-2020 Task 11: A Kitchen Solution to Automatic Propaganda Detection
Elena Mikhalkova | Nadezhda Ganzherli | Anna Glazkova | Yuliya Bidulya
Proceedings of the Fourteenth Workshop on Semantic Evaluation

The article describes a fast solution to propaganda detection at SemEval-2020 Task 11, based on feature adjustment. We use per-token vectorization of features and a simple Logistic Regression classifier to quickly test different hypotheses about our data. We come up with what seems to us the best solution, however, we are unable to align it with the result of the metric suggested by the organizers of the task. We test how our system handles class and feature imbalance by varying the number of samples of two classes (Propaganda and None) in the training set, the size of a context window in which a token is vectorized and combination of vectorization means. The result of our system at SemEval2020 Task 11 is F-score=0.37.