Dependency Parsing via Sequence Generation

Dependency parsing aims to extract syntactic dependency structure or semantic dependency structure for sentences. Existing methods for dependency parsing include transition-based method, graph-based method and sequence-tosequence method. These methods obtain excellent performance and we notice them belong to labeling method. Therefore, it may be very valuable and interesting to explore the possibility of using generative method to implement dependency parsing. In this paper, we propose to achieve Dependency Parsing (DP) via Sequence Generation (SG) by utilizing only the pre-trained language model without any auxiliary structures. We first explore different serialization designing strategies for converting parsing structures into sequences. Then we design dependency units and concatenate these units into the sequence for DPSG. We verify the DPSG is capable of parsing on widely used DP benchmarks, i.e., PTB, UD2.2, SDP15 and SemEval16. In addition, we also investigate the astonishing low-resource applicability of DPSG, which includes unsupervised cross-domain conducted on CODT and fewshot cross-task conducted on SDP15. Our research demonstrates that sequence generation is one of the effective methods to achieve dependency parsing. Our codes are available now. 1 .