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