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.

本文描述了 SemEval-2022 任务 8 排行榜上排名第二的系统。我们提出了一种基于实体增强的连体 Transformer，根据新闻文章中讨论的事件的共享叙述、实体、位置和时间等不同子维度计算新闻文章相似性。