@inproceedings{kuimov-etal-2022-skoltechnlp,
title = "{S}koltech{NLP} at {S}em{E}val-2022 Task 8: Multilingual News Article Similarity via Exploration of News Texts to Vector Representations",
author = "Kuimov, Mikhail and
Dementieva, Daryna and
Panchenko, Alexander",
editor = "Emerson, Guy and
Schluter, Natalie and
Stanovsky, Gabriel and
Kumar, Ritesh and
Palmer, Alexis and
Schneider, Nathan and
Singh, Siddharth and
Ratan, Shyam",
booktitle = "Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.semeval-1.160",
doi = "10.18653/v1/2022.semeval-1.160",
pages = "1136--1144",
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.",
}
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%0 Conference Proceedings
%T SkoltechNLP at SemEval-2022 Task 8: Multilingual News Article Similarity via Exploration of News Texts to Vector Representations
%A Kuimov, Mikhail
%A Dementieva, Daryna
%A Panchenko, Alexander
%Y Emerson, Guy
%Y Schluter, Natalie
%Y Stanovsky, Gabriel
%Y Kumar, Ritesh
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y Singh, Siddharth
%Y Ratan, Shyam
%S Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F kuimov-etal-2022-skoltechnlp
%X 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.
%R 10.18653/v1/2022.semeval-1.160
%U https://aclanthology.org/2022.semeval-1.160
%U https://doi.org/10.18653/v1/2022.semeval-1.160
%P 1136-1144
Markdown (Informal)
[SkoltechNLP at SemEval-2022 Task 8: Multilingual News Article Similarity via Exploration of News Texts to Vector Representations](https://aclanthology.org/2022.semeval-1.160) (Kuimov et al., SemEval 2022)
ACL