Neural Network based Deep Transfer Learning for Cross-domain Dependency Parsing
This is an incremental improvement for NLP researchers working on domain adaptation in dependency parsing.
The authors tackled cross-domain dependency parsing by adapting a stack-pointer network with self-attention and deep transfer learning to three target domains (product comments, blogs, and web fiction), achieving competitive performance.
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.