1 research outputs found
Neural Network based Deep Transfer Learning for Cross-domain Dependency Parsing
In this paper, we describe the details of the neural dependency parser
sub-mitted by our team to the NLPCC 2019 Shared Task of Semi-supervised do-main
adaptation subtask on Cross-domain Dependency Parsing. Our system is based on
the stack-pointer networks(STACKPTR). Considering the im-portance of context,
we utilize self-attention mechanism for the representa-tion vectors to capture
the meaning of words. In addition, to adapt three dif-ferent domains, we
utilize neural network based deep transfer learning which transfers the
pre-trained partial network in the source domain to be a part of deep neural
network in the three target domains (product comments, product blogs and web
fiction) respectively. Results on the three target domains demonstrate that our
model performs competitively.Comment: paper for NLPCC 2019 Shared Task of Semi-supervised domain adaptation
subtask on Cross-domain Dependency Parsin