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This work is about finding the similarity between a pair of news articles.There are seven different objective similarity metrics provided in the datasetfor each pair and the news articles are in multiple different languages. On topof the pre-trained embedding model, we calculated cosine similarity forbaseline results and feed-forward neural network was then trained on top of itto improve the results. We also built separate pipelines for each similaritymetric for feature extraction. We could see significant improvement frombaseline results using feature extraction and feed-forward neural network.

Wolfies at SemEval-2022 Task 8: Feature extraction pipeline with transformers for Multi-lingual news article similarity