WueDevils at SemEval-2022 Task 8: Multilingual News Article Similarity via Pair-Wise Sentence Similarity Matrices

Dirk Wangsadirdja, Felix Heinickel, Simon Trapp, Albin Zehe, Konstantin Kobs, Andreas Hotho


Abstract
We present a system that creates pair-wise cosine and arccosine sentence similarity matrices using multilingual sentence embeddings obtained from pre-trained SBERT and Universal Sentence Encoder (USE) models respectively. For each news article sentence, it searches the most similar sentence from the other article and computes an average score. Further, a convolutional neural network calculates a total similarity score for the article pairs on these matrices. Finally, a random forest regressor merges the previous results to a final score that can optionally be extended with a publishing date score.
Anthology ID:
2022.semeval-1.175
Volume:
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
Month:
July
Year:
2022
Address:
Seattle, United States
Venue:
SemEval
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
1235–1243
Language:
URL:
https://aclanthology.org/2022.semeval-1.175
DOI:
10.18653/v1/2022.semeval-1.175
Bibkey:
Cite (ACL):
Dirk Wangsadirdja, Felix Heinickel, Simon Trapp, Albin Zehe, Konstantin Kobs, and Andreas Hotho. 2022. WueDevils at SemEval-2022 Task 8: Multilingual News Article Similarity via Pair-Wise Sentence Similarity Matrices. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pages 1235–1243, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
WueDevils at SemEval-2022 Task 8: Multilingual News Article Similarity via Pair-Wise Sentence Similarity Matrices (Wangsadirdja et al., SemEval 2022)
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