26 citations · 31 across the 5 of their papers we have counts for
9 papers
Intermediate Entity-based Sparse Interpretable Representation Learning
Diego Garcia-Olano, Yasumasa Onoe, Joydeep Ghosh +1
Interpretable entity representations (IERs) are sparse embeddings that are "human-readable" in that dimensions correspond to fine-grained entity types and values are predicted prob…
Entity Cloze By Date: What LMs Know About Unseen Entities
Yasumasa Onoe, Michael J. Q. Zhang, Eunsol Choi +1
Language models (LMs) are typically trained once on a large-scale corpus and used for years without being updated. However, in a dynamic world, new entities constantly arise. We pr…
Cross-Lingual Fine-Grained Entity Typing
Nila Selvaraj, Yasumasa Onoe, Greg Durrett
The growth of cross-lingual pre-trained models has enabled NLP tools to rapidly generalize to new languages. While these models have been applied to tasks involving entities, their…
CREAK: A Dataset for Commonsense Reasoning over Entity Knowledge
Yasumasa Onoe, Michael J. Q. Zhang, Eunsol Choi +1
Most benchmark datasets targeting commonsense reasoning focus on everyday scenarios: physical knowledge like knowing that you could fill a cup under a waterfall [Talmor et al., 201…
Biomedical Interpretable Entity Representations
Diego Garcia-Olano, Yasumasa Onoe, Ioana Baldini +3
Pre-trained language models induce dense entity representations that offer strong performance on entity-centric NLP tasks, but such representations are not immediately interpretabl…
Modeling Fine-Grained Entity Types with Box Embeddings
Yasumasa Onoe, Michael Boratko, Andrew McCallum +1
Neural entity typing models typically represent fine-grained entity types as vectors in a high-dimensional space, but such spaces are not well-suited to modeling these types' compl…