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Fine tuning Pre Trained Transformer Language Models to Distantly Supervised Relation Extraction

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Figure

Figure 1: Distant supervision generates noisily labeledrelation mentions by aligning entity tuples in a text cor-pus with relation instances from a knowledge base.
Figure 2: Transformer-Block architecture and trainingobjectives. A Transformer-Block is applied at each ofthe L layers to produce states h1 to hL
Figure 3: Relation extraction requires a structured inputfor fine-tuning, with special delimiters to assign differ-of the previous layerbedding and the byte pair embedding for each token.Statesent meanings to parts of the input
Figure 4: Precision-Recall curve on the NYT dataset.Our method (DISTRE) shows a more balanced perfor-dicates results we obtained with the OpenNREmance across relations, especially in the long tail
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