Adversarial training for multi-context joint entity and relation extraction

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Authors Johannes Deleu, Chris Develder, Thomas Demeester, Giannis Bekoulis
Journal/Conference Name EMNLP 2018 10
Paper Category
Paper Abstract Adversarial training (AT) is a regularization method that can be used to improve the robustness of neural network methods by adding small perturbations in the training data. We show how to use AT for the tasks of entity recognition and relation extraction. In particular, we demonstrate that applying AT to a general purpose baseline model for jointly extracting entities and relations, allows improving the state-of-the-art effectiveness on several datasets in different contexts (i.e., news, biomedical, and real estate data) and for different languages (English and Dutch).
Date of publication 2018
Code Programming Language Python
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