Recursive Non-Autoregressive Graph-to-Graph Transformer for Dependency Parsing with Iterative Refinement
Abstract
We propose the Recursive Non-autoregressive Graph-to-Graph Transformer architecture (RNGTr) for the iterative refinement of arbitrary graphs through the recursive application of a non-autoregressive Graph-to-Graph Transformer and apply it to syntactic dependency parsing. We demonstrate the power and effectiveness of RNGTr on several dependency corpora, using a refinement model pre-trained with BERT. We also introduce Syntactic Transformer (SynTr), a non-recursive parser similar to our refinement model. RNGTr can improve the accuracy of a variety of initial parsers on 13 languages from the Universal Dependencies Treebanks, English and Chinese Penn Treebanks, and the German CoNLL2009 corpus, even improving over the new state-of-the-art results achieved by SynTr, significantly improving the state-of-the-art for all corpora tested.
Keywords
Cite
@article{arxiv.2003.13118,
title = {Recursive Non-Autoregressive Graph-to-Graph Transformer for Dependency Parsing with Iterative Refinement},
author = {Alireza Mohammadshahi and James Henderson},
journal= {arXiv preprint arXiv:2003.13118},
year = {2021}
}
Comments
Accepted to Transactions of the Association for Computational Linguistics (TACL) journal