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Titre : Deep Learning for Unsupervised Relation Extraction
Date : 5 juillet 2022
Heure: 15:00
Résumé : Capturing concepts' interrelations is a fundamental of natural language understanding. It constitutes a bridge between two historically separate approaches of artificial intelligence: the use of symbolic and distributed representations. However, tackling this problem without human supervision poses several issues, and unsupervised models have difficulties echoing the expressive breakthroughs of supervised ones. This thesis addresses two supervision gaps we identified: the problem of regularization of sentence-level discriminative models and the problem of leveraging relational information from dataset-level structures. The first gap arises following the increased use of discriminative approaches, such as deep neural network classifiers, in the supervised setting. These models tend to collapse without supervision. To overcome this limitation, we introduce two relation distribution losses to constrain the relation classifier into a trainable state. The second gap arises from
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