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Found 11 papers, 5 papers with code
Label Verbalization and Entailment for Effective Zero and Few-Shot Relation Extraction
In our experiments on TACRED we attain 63% F1 zero-shot, 69% with 16 examples per relation (17% points better than the best supervised system on the same conditions), and only 4 points short to the state-of-the-art (which uses 20 times more training data).
Ranking Over Scoring: Towards Reliable and Robust Automated Evaluation of LLM-Generated Medical Explanatory Arguments
Evaluating LLM-generated text has become a key challenge, especially in domain-specific contexts like the medical field.
EriBERTa: A Bilingual Pre-Trained Language Model for Clinical Natural Language Processing
We demonstrate that EriBERTa outperforms previous Spanish language models in the clinical domain, showcasing its superior capabilities in understanding medical texts and extracting meaningful information.
Label Verbalization and Entailment for Effective Zero- and Few-Shot Relation Extraction
In our experiments on TACRED we attain 63% F1 zero-shot, 69% with 16 examples per relation (17% points better than the best supervised system on the same conditions), and only 4 points short to the state-of-the-art (which uses 20 times more training data).
Ranked #11 on
Relation Extraction
on TACRED
Give your Text Representation Models some Love: the Case for Basque
This is suboptimal as, for many languages, the models have been trained on smaller (or lower quality) corpora.
Learning Text Representations for 500K Classification Tasks on Named Entity Disambiguation
Named Entity Disambiguation algorithms typically learn a single model for all target entities.
Studying the Wikipedia Hyperlink Graph for Relatedness and Disambiguation
Hyperlinks and other relations in Wikipedia are a extraordinary resource which is still not fully understood.
Matching Cultural Heritage items to Wikipedia
Digitised Cultural Heritage (CH) items usually have short descriptions and lack rich contextual information.
