Natural language understanding tasks such as open-domain question answering often
require retrieving and assimilating factual information from multiple sources. We propose to …

Learning representations of entity mentions is a core component of modern entity linking
systems for both candidate generation and making linking predictions. In this paper, we …

Entity linking models have achieved significant success via utilizing pretrained language
models to capture semantic features. However, the NIL prediction problem, which aims to …

Existing work on Entity Linking mostly assumes that the reference knowledge base is
complete, and therefore all mentions can be linked. In practice this is hardly ever the case …

Dual encoder models are ubiquitous in modern classification and retrieval. Crucial for
training such dual encoders is an accurate estimation of gradients from the partition function …

Large-scale, high-quality corpora are critical for advancing research in coreference
resolution. However, existing datasets vary in their definition of coreferences and have been …

Entity Linking (EL) is the gateway into Knowledge Bases. Recent advances in EL utilize
dense retrieval approaches for Candidate Generation, which addresses some of the …

In this information-accumulating world, each of us must learn continuously. To participate in
a new field, or even a sub-field, one must be aware of the terminology including the …

Entity linking (EL) is the computational process of connecting textual mentions to
corresponding entities. Like many areas of natural language processing, the EL field has …

End-to-end multilingual entity linking (MEL) is concerned with identifying multilingual entity
mentions and their corresponding entity IDs in a knowledge base. Existing works assumed …