LSX_team5 at SemEval-2022 Task 8: Multilingual News Article Similarity Assessment based on Word- and Sentence Mover’s Distance

Stefan Heil, Karina Kopp, Albin Zehe, Konstantin Kobs, Andreas Hotho


Abstract
This paper introduces our submission for the SemEval 2022 Task 8: Multilingual News Article Similarity. The task of the competition consisted of the development of a model, capable of determining the similarity between pairs of multilingual news articles. To address this challenge, we evaluated the Word Mover’s Distance in conjunction with word embeddings from ConceptNet Numberbatch and term frequencies of WorldLex, as well the Sentence Mover’s Distance based on sentence embeddings generated by pretrained transformer models of Sentence-BERT. To facilitate the comparison of multilingual articles with Sentence-BERT models, we deployed a Neural Machine Translation system. All our models achieve stable results in multilingual similarity estimation without learning parameters.
Anthology ID:
2022.semeval-1.168
Volume:
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
Month:
July
Year:
2022
Address:
Seattle, United States
Venue:
SemEval
SIGs:
SIGLEX | SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
1190–1195
Language:
URL:
https://aclanthology.org/2022.semeval-1.168
DOI:
10.18653/v1/2022.semeval-1.168
Bibkey:
Cite (ACL):
Stefan Heil, Karina Kopp, Albin Zehe, Konstantin Kobs, and Andreas Hotho. 2022. LSX_team5 at SemEval-2022 Task 8: Multilingual News Article Similarity Assessment based on Word- and Sentence Mover’s Distance. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pages 1190–1195, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
LSX_team5 at SemEval-2022 Task 8: Multilingual News Article Similarity Assessment based on Word- and Sentence Mover’s Distance (Heil et al., SemEval 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.semeval-1.168.pdf
Video:
 https://aclanthology.org/2022.semeval-1.168.mp4
Data
ConceptNet