论文标题
零拍的跨语言实体链接中的跨语性转移
Cross-Lingual Transfer in Zero-Shot Cross-Language Entity Linking
论文作者
论文摘要
跨语言实体将链接的理由提及多种语言与单语言知识库。我们为此任务提出了一个神经排名架构，该架构使用了神经网络中的多语言BERT表示和上下文。我们发现，BERT的多语言能力在单语和多语言设置中带来了强大的性能。此外，我们探索零击语言转移，并找到出人意料的表现。我们研究了零射的降解，发现它可以通过提出的辅助训练目标来部分缓解，但是剩余的错误最好归因于域移位而不是语言传递。
Cross-language entity linking grounds mentions in multiple languages to a single-language knowledge base. We propose a neural ranking architecture for this task that uses multilingual BERT representations of the mention and the context in a neural network. We find that the multilingual ability of BERT leads to robust performance in monolingual and multilingual settings. Furthermore, we explore zero-shot language transfer and find surprisingly robust performance. We investigate the zero-shot degradation and find that it can be partially mitigated by a proposed auxiliary training objective, but that the remaining error can best be attributed to domain shift rather than language transfer.
