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This paper describes the second-placed system on the leaderboard ofSemEval-2022 Task 8: Multilingual News Article Similarity. We propose anentity-enriched Siamese Transformer which computes news article similaritybased on different sub-dimensions, such as the shared narrative, entities,location and time of the event discussed in the news article. Our systemexploits a Siamese network architecture using a Transformer encoder to learndocument-level representations for the purpose of capturing the narrativetogether with the auxiliary entity-based features extracted from the newsarticles. The intuition behind using all these features together is to capturethe similarity between news articles at different granularity levels and toassess the extent to which different news outlets write about "the sameevents". Our experimental results and detailed ablation study demonstrate theeffectiveness and the validity of our proposed method.

GateNLP-UShef at SemEval-2022 Task 8: Entity-Enriched Siamese Transformer for Multilingual News Article Similarity