Document Type

Conference Proceeding

Publication Title

CEUR Workshop Proceedings

Abstract

The wide-spread use of social networks has given rise to subjective, misleading, and even false information on the Internet. Thus, subjectivity detection can play an important role in ensuring the objectiveness and the quality of a piece of information. This paper presents the solution built by the Gpachov team for the CLEF-2023 CheckThat! lab Task 2 on subjectivity detection. Three different research directions are explored. The first one is based on fine-tuning a sentence embeddings encoder model and dimensionality reduction. The second one explores a sample-efficient few-shot learning model. The third one evaluates fine-tuning a multilingual transformer on an altered dataset, using data from multiple languages. Finally, the three approaches are combined in a simple majority voting ensemble, resulting in 0.77 macro F1 on the test set and achieving 2nd place on the English subtask.

First Page

404

Last Page

412

Publication Date

9-2023

Keywords

Deep Learning, Ensemble, Few-shot learning, Natural Language Processing, Sentence Embeddings, Subjectivity detection, Transformer

Comments

Archived, thanks to CEUR Workshop Proceedings

License: CC by 4.0

Uploaded: 18 March 2024

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