Simon Trapp


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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
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)

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.