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Adversarial training for multi-context joint entity and relation extraction

2018-08-21EMNLP 2018Code Available0· sign in to hype

Giannis Bekoulis, Johannes Deleu, Thomas Demeester, Chris Develder

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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).

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Benchmark Results

DatasetModelMetricClaimedVerifiedStatus
ACE 2004multi-head + ATRE+ Micro F147.45Unverified
Adverse Drug Events (ADE) Corpusmulti-head + ATRE+ Macro F175.52Unverified
CoNLL04multi-head + ATRE+ Macro F1 61.95Unverified

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