Neural Transition-based Syntactic Linearization
Linfeng Song, Yue Zhang, Daniel Gildea · 2018
The task of linearization is to find a grammatical order given a set of words.Traditional models use statistical methods.Syntactic linearization systems, which generate a sentence along with its syntactic tree, have shown state-of-the-art performance.Recent work shows that a multilayer LSTM language model outperforms competitive statistical syntactic linearization systems without using syntax.In this paper, we study neural syntactic linearization, building a transition-based syntactic linearizer leveraging a feed forward neural network, observing significantly better results compared to LSTM language models on this task.