Papers with Universal Dependencies Treebanks

2 papers
Recursive Non-Autoregressive Graph-to-Graph Transformer for Dependency Parsing with Iterative Refinement (2021.tacl-1)

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Challenge: RNGTr is a non-recursive Graph-to-Graph Transformer for iterative refinement of graphs . it can improve the accuracy of initial parsers on 13 languages .
Approach: They propose a recursive non-autoregressive Graph-to-Graph Transformer architecture for iterative refinement of arbitrary graphs and apply it to syntactic dependency parsing.
Outcome: The proposed architecture can improve state-of-the-art on 13 languages and the German CoNLL2009 corpus.
Graph-to-Graph Transformer for Transition-based Dependency Parsing (2020.findings-emnlp)

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Challenge: Existing models for conditioning on graphs and predicting graphs are weak, but they are effective for transition-based dependency parsing.
Approach: They propose a Transformer architecture for conditioning on and predicting arbitrary graphs.
Outcome: The proposed architecture outperforms the state-of-the-art in transition-based dependency parsing on English Penn Treebank and 13 languages of Universal Dependencies Treebanks.

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