EMBEDDIA at SemEval-2022 Task 8: Investigating Sentence, Image, and Knowledge Graph Representations for Multilingual News Article Similarity

Elaine Zosa, Emanuela Boros, Boshko Koloski, Lidia Pivovarova

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Abstract

In this paper, we present the participation of the EMBEDDIA team in the SemEval-2022 Task 8 (Multilingual News Article Similarity). We cover several techniques and propose different methods for finding the multilingual news article similarity by exploring the dataset in its entirety. We take advantage of the textual content of the articles, the provided metadata (e.g., titles, keywords, topics), the translated articles, the images (those that were available), and knowledge graph-based representations for entities and relations present in the articles. We, then, compute the semantic similarity between the different features and predict through regression the similarity scores. Our findings show that, while our proposed methods obtained promising results, exploiting the semantic textual similarity with sentence representations is unbeatable. Finally, in the official SemEval-2022 Task 8, we ranked fifth in the overall team ranking cross-lingual results, and second in the English-only results.
Original languageEnglish
Title of host publicationProceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
Place of PublicationStroudsburg
PublisherThe Association for Computational Linguistics
Publication date11 Jul 2022
Pages1107–1113
ISBN (Electronic)978-1-955917-80-3
Publication statusPublished - 11 Jul 2022
MoE publication typeA4 Article in conference proceedings
EventInternational Workshop on Semantic Evaluation - Seattle, United States, Seattle, United States
Duration: 14 Jul 202215 Jul 2022
Conference number: 16
https://semeval.github.io/SemEval2022/

Fields of Science

  • 113 Computer and information sciences

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