Entity Linking is one of the most common Natural Language Processing tasks in practical applications, but so far efficient end-to-end solutions with multilingual coverage have been lacking, leading to complex model stacks.
Inspired by duck typing in programming languages, we propose to define the type of an entity based on the relations that it has with other entities in a knowledge graph.
Language models (LMs) exhibit remarkable abilities to solve new tasks from just a few examples or textual instructions, especially at scale.
This paper created the Unknown Entity Discovery and Indexing (EDIN) benchmark where unknown entities, that is entities without a description in the knowledge base and labeled mentions, have to be integrated into an existing entity linking system.
Moreover, in a zero-shot setting on languages with no training data at all, mGENRE treats the target language as a latent variable that is marginalized at prediction time.
Ranked #2 on Entity Disambiguation on Mewsli-9 (using extra training data)
Email responses often contain items-such as a file or a hyperlink to an external document-that are attached to or included inline in the body of the message.
In this paper we explore a task-driven approach to interfacing NLP components, where language processing is guided by the end-task that each application requires.