SkoltechNLP at SemEval-2021 Task 2: Generating Cross-Lingual Training Data for the Word-in-Context Task

Razzhigaev, A.; Arefyev, N.; Panchenko, A.

Авторы: Razzhigaev Anton, Arefyev Nikolay, Panchenko Alexander
Сборник: Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)
Год издания: 2021
Издательство: Argentinean Association of Computational Mechanics
Местоположение издательства: Argentina
Первая страница: 157
Последняя страница: 162
DOI: 10.18653/v1/2021.semeval-1.16
Аннотация: In this paper, we present a system for the solution of the cross-lingual and multilingual word-in-context disambiguation task. Task organizers provided monolingual data in several languages, but no cross-lingual training data were available. To address the lack of the officially provided cross-lingual training data, we decided to generate such data ourselves. We describe a simple yet effective approach based on machine translation and back translation of the lexical units to the original language used in the context of this shared task. In our experiments, we used a neural system based on the XLM-R, a pre-trained transformer-based masked language model, as a baseline. We show the effectiveness of the proposed approach as it allows to substantially improve the performance of this strong neural baseline model. In addition, in this study, we present multiple types of the XLM-R based classifier, experimenting with various ways of mixing information from the first and second occurrences of the target word in two samples.
Добавил в систему: Арефьев Николай Викторович

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SkoltechNLP at SemEval-2021 Task 2: Generating Cross-Lingual Training Data for the Word-in-Context Taskстатья

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