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) · 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 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.