4 citations · 4 across the 3 of their papers we have counts for
3 papers
cs.CL2023
Entity or Relation Embeddings? An Analysis of Encoding Strategies for Relation Extraction
Frank Mtumbuka, Steven Schockaert
Relation extraction is essentially a text classification problem, which can be tackled by fine-tuning a pre-trained language model (LM). However, a key challenge arises from the fa…
cs.CL2023
EnCore: Fine-Grained Entity Typing by Pre-Training Entity Encoders on Coreference Chains
Frank Mtumbuka, Steven Schockaert
Entity typing is the task of assigning semantic types to the entities that are mentioned in a text. In the case of fine-grained entity typing (FET), a large set of candidate type l…
cs.CL2022★ 4 cited
Beyond Distributional Hypothesis: Let Language Models Learn Meaning-Text Correspondence
Myeongjun Jang, Frank Mtumbuka, Thomas Lukasiewicz
The logical negation property (LNP), which implies generating different predictions for semantically opposite inputs, is an important property that a trustworthy language model mus…