SkoltechNLP at SemEval-2022 Task 8: Multilingual News Article Similarity via Exploration of News Texts to Vector Representations

Mikhail Kuimov, Daryna Dementieva, Alexander Panchenko


Abstract
This paper describes our contribution to SemEval 2022 Task 8: Multilingual News Article Similarity. The aim was to test completely different approaches and distinguish the best performing. That is why we’ve considered systems based on Transformer-based encoders, NER-based, and NLI-based methods (and their combination with SVO dependency triplets representation). The results prove that Transformer models produce the best scores. However, there is space for research and approaches that give not yet comparable but more interpretable results.
Anthology ID:
2022.semeval-1.160
Volume:
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
Month:
July
Year:
2022
Address:
Seattle, United States
Editors:
Guy Emerson, Natalie Schluter, Gabriel Stanovsky, Ritesh Kumar, Alexis Palmer, Nathan Schneider, Siddharth Singh, Shyam Ratan
Venue:
SemEval
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
1136–1144
Language:
URL:
https://aclanthology.org/2022.semeval-1.160
DOI:
10.18653/v1/2022.semeval-1.160
Bibkey:
Cite (ACL):
Mikhail Kuimov, Daryna Dementieva, and Alexander Panchenko. 2022. SkoltechNLP at SemEval-2022 Task 8: Multilingual News Article Similarity via Exploration of News Texts to Vector Representations. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pages 1136–1144, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
SkoltechNLP at SemEval-2022 Task 8: Multilingual News Article Similarity via Exploration of News Texts to Vector Representations (Kuimov et al., SemEval 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.semeval-1.160.pdf
Video:
 https://aclanthology.org/2022.semeval-1.160.mp4
Code
 skoltech-nlp/multilingual_news_similarity