A teacher-student pipeline in which GPT-4o diagnoses a student's errors, assigns difficulty scores, and generates remediations, then trains a smaller model by curriculum learning, reports new state-of-the-art F1 on four PPI datasets and DDI.
What do You Mean by Relation Extraction? A Survey on Datasets and Study on Scientific Relation Classification
1 Pith paper cite this work. Polarity classification is still indexing.
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abstract
Over the last five years, research on Relation Extraction (RE) witnessed extensive progress with many new dataset releases. At the same time, setup clarity has decreased, contributing to increased difficulty of reliable empirical evaluation (Taill\'e et al., 2020). In this paper, we provide a comprehensive survey of RE datasets, and revisit the task definition and its adoption by the community. We find that cross-dataset and cross-domain setups are particularly lacking. We present an empirical study on scientific Relation Classification across two datasets. Despite large data overlap, our analysis reveals substantial discrepancies in annotation. Annotation discrepancies strongly impact Relation Classification performance, explaining large drops in cross-dataset evaluations. Variation within further sub-domains exists but impacts Relation Classification only to limited degrees. Overall, our study calls for more rigour in reporting setups in RE and evaluation across multiple test sets.
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Error-Aware Curriculum Learning for Biomedical Relation Classification
A teacher-student pipeline in which GPT-4o diagnoses a student's errors, assigns difficulty scores, and generates remediations, then trains a smaller model by curriculum learning, reports new state-of-the-art F1 on four PPI datasets and DDI.