Gorynych Transformer at SemEval-2020 Task 6: Multi-task Learning for Definition Extraction

Davletov A.; Nikolay Arefyev; Shatilov A.; Gordeev D.; Rey A.

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Gorynych Transformer at SemEval-2020 Task 6: Multi-task Learning for Definition Extraction

P. 487–493.

Davletov A., Nikolay Arefyev, Shatilov A., Gordeev D., Rey A.

This paper describes our approach to “DeftEval: Extracting Definitions from Free Text in Textbooks” competition held as a part of Semeval 2020. The task was devoted to finding and labeling definitions in texts. DeftEval was split into three subtasks: sentence classification, sequence labeling and relation classification. Our solution ranked 5th in the first subtask and 23rd and 21st in the second and the third subtasks respectively. We applied simultaneous multi-task learning with Transformer-based models for subtasks 1 and 3 and a single BERT-based model for named entity recognition.

Language: English

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Keywords: Transformer definition extraction multi-task learning

Publication based on the results of:

Development of Mathematical Models and Methods for Recommender Systems and Natural Language Processing (2020)

In book

Proceedings of the 14th International Workshop on Semantic Evaluation (SemEval-2020)

Association for Computational Linguistics, 2020.

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Forecasting stock indices remains an important yet difficult problem in financial markets. Many studies have therefore explored deep learning methods for this task. Over the past few decades, Recurrent Neural Network (RNN)- and Long Short- Term Memory (LSTM)-based models have dominated much of the stock prediction literature, and more recently Transformerbased methods have also shown promising ...

Added: May 10, 2026

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The growing demand for personalized human-computer interaction calls for methods that jointly model emotional states and personality traits. However, large-scale multimodal corpora annotated for both tasks are still lacking. This challenge stems from integrating diverse, task-specific corpora with divergent modality informativeness and domain characteristics. To address it, we propose SSL-MEPR, a semi-supervised multi-task cross-domain learning ...

Added: February 27, 2026

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Psychological and neurological studies earlier suggested that a personality type can be determined by the whole face as well as by its sides. This article discusses novel research using deep neural networks that address the features of both sides of the face (hemifaces) to assess the human’s Big Five personality traits (PT). For this, we have developed a real time approach called EmoFormer ...

Added: March 6, 2025

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This article presents a research methodology for audio–visual speech recognition (AVSR) in driver assistive systems. These systems necessitate ongoing interaction with drivers while driving through voice control for safety reasons. The article introduces a novel audio–visual speech command recognition transformer (AVCRFormer) specifically designed for robust AVSR. We propose (i) a multimodal fusion strategy based on ...

Added: March 6, 2025

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D. D. Bakshandaeva, Dimitrov D. V., Arkhipkin V. S. et al., Computer Optics 2023 Vol. 47 No. 1 P. 185–195

Supporting the current trend in the AI community, we present the AI Journey 2021 Challenge called FusionBrain, the first competition which is targeted to make a universal architecture which could process different modalities (in this case, images, texts, and code) and solve multiple tasks for vision and language. The FusionBrain Challenge combines the following specific tasks: Code2code ...

Added: June 13, 2023

Advances in Neural Computation, Machine Learning, and Cognitive Research VI : Selected Papers from the XXIV International Conference on Neuroinformatics, October 17-21, 2022, Moscow, Russia

Mistryukova L., Derkach D., Hushchyn M. et al., Springer, 2022.

Methods of Stokes profile inversion based on spectral polarization analysis represent a powerful tool for obtaining information on magnetic and thermodynamic properties in the solar atmosphere. However, these methods involve solving the radiation transport equation. Over the past decades, several approaches have been developed to provide an analytical solution to the inverse problem, but despite ...

Added: October 28, 2022

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Savchenko A., / Series Computer Science "arxiv.org". 2022.

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Added: October 21, 2022

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Chirkova N., Troshin S., , in: ESEC/FSE 2021: Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering.: Association for Computing Machinery (ACM), 2021. P. 703–715.

Initially developed for natural language processing (NLP), Transformers are now widely used for source code processing, due to the format similarity between source code and text. In contrast to natural language, source code is strictly structured, i.e., it follows the syntax of the programming language. Several recent works develop Transformer modifications for capturing syntactic information ...

Added: August 31, 2021

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Added: August 31, 2021

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Gordeev D., Davletov A., Rey A. et al., , in: Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation.: COLING, 2020. P. 45–49.

In this paper, we describe the results of team LIORI at the FinCausal 2020 Shared task held as a part of the 1st Joint Workshop on Financial Narrative Processing and MultiLingual Financial Summarisation. The shared task consisted of two subtasks: classifying whether a sentence contains any causality and labelling phrases that indicate causes and consequences. ...

Added: December 7, 2020