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Entity retrieval--retrieving information about entity mentions in a query--isa key step in open-domain tasks, such as question answering or fact checking.However, state-of-the-art entity retrievers struggle to retrieve rare entitiesfor ambiguous mentions due to biases towards popular entities. Incorporatingknowledge graph types during training could help overcome popularity biases,but there are several challenges: (1) existing type-based retrieval methodsrequire mention boundaries as input, but open-domain tasks run on unstructuredtext, (2) type-based methods should not compromise overall performance, and (3)type-based methods should be robust to noisy and missing types. In this work,we introduce TABi, a method to jointly train bi-encoders on knowledge graphtypes and unstructured text for entity retrieval for open-domain tasks. TABileverages a type-enforced contrastive loss to encourage entities and queries ofsimilar types to be close in the embedding space. TABi improves retrieval ofrare entities on the Ambiguous Entity Retrieval (AmbER) sets, while maintainingstrong overall retrieval performance on open-domain tasks in the KILT benchmarkcompared to state-of-the-art retrievers. TABi is also robust to incomplete typesystems, improving rare entity retrieval over baselines with only 5% typecoverage of the training dataset. We make our code publicly available athttps://github.com/HazyResearch/tabi.

TABi: Type-Aware Bi-Encoders for Open-Domain Entity Retrieval