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Megan Leszczynski

3 accepted papers

2022

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

ACL 2022findings

Entity retrieval—retrieving information about entity mentions in a query—is a key step in open-domain tasks, such as question answering or fact checking. However, state-of-the-art entity retrievers struggle to retrieve rare entities for ambiguous mentions due to biases towards popular entities. Inco…

2021

Cross-Domain Data Integration for Named Entity Disambiguation in Biomedical Text

EMNLP 2021finding

Named entity disambiguation (NED), which involves mapping textual mentions to structured entities, is particularly challenging in the medical domain due to the presence of rare entities. Existing approaches are limited by the presence of coarse-grained structural resources in biomedical knowledge ba…

2020

Kaleidoscope: An Efficient, Learnable Representation For All Structured Linear Maps

ICLR 2020spotlight

Modern neural network architectures use structured linear transformations, such as low-rank matrices, sparse matrices, permutations, and the Fourier transform, to improve inference speed and reduce memory usage compared to general linear maps. However, choosing which of the myriad structured transfo…

Cited by 71SourceScholar