Unsupervised Pre-training With Seq2Seq Reconstruction Loss for Deep Relation Extraction Models
نویسندگان
چکیده
Relation extraction models based on deep learning have been attracting a lot of attention recently. Little research is carried out to reduce their need of labeled training data. In this work, we propose an unsupervised pre-training method based on the sequence-to-sequence model for deep relation extraction models. The pre-trained models need only half or even less training data to achieve equivalent performance as the same models without pre-training.
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