%0 Conference Proceedings %T WueDevils at SemEval-2022 Task 8: Multilingual News Article Similarity via Pair-Wise Sentence Similarity Matrices %A Wangsadirdja, Dirk %A Heinickel, Felix %A Trapp, Simon %A Zehe, Albin %A Kobs, Konstantin %A Hotho, Andreas %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 wangsadirdja-etal-2022-wuedevils %X 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. %R 10.18653/v1/2022.semeval-1.175 %U https://aclanthology.org/2022.semeval-1.175 %U https://doi.org/10.18653/v1/2022.semeval-1.175 %P 1235-1243