Adversarial training for multi-context joint entity and relation extraction

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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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Relation Extraction ACE 2004 multi-head + AT NER Micro F1 81.64 # 8
RE+ Micro F1 47.45 # 7
Cross Sentence No # 1
Relation Extraction Adverse Drug Events (ADE) Corpus multi-head + AT RE+ Macro F1 75.52 # 13
NER Macro F1 86.73 # 12
Relation Extraction CoNLL04 multi-head + AT NER Macro F1 83.6 # 7
RE+ Macro F1 61.95 # 8


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