%Aigaion2 BibTeX export from Idiap Publications
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@ARTICLE{Mohammadshahi_TACL-2_2020,
         author = {Mohammadshahi, Alireza and Henderson, James},
       projects = {Idiap},
          month = oct,
          title = {Recursive Non-Autoregressive Graph-to-Graph Transformer for Dependency Parsing with Iterative Refinement},
        journal = {Transactions of the Association for Computational Linguistics},
           year = {2020},
            url = {https://arxiv.org/abs/2003.13118},
       crossref = {Mohammadshahi_TACL_2020},
       abstract = {We propose the Recursive Non-autoregressive Graph-to-graph Transformer architecture (RNG-Tr) 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. The Graph-to-Graph Transformer architecture of \newcite{mohammadshahi2019graphtograph} has previously been used for autoregressive graph prediction, but here we use it to predict all edges of the graph independently, conditioned on a previous prediction of the same graph. We demonstrate the power and effectiveness of RNG-Tr on several dependency corpora, using a refinement model pre-trained with BERT. We also introduce Dependency BERT (DepBERT), a non-recursive parser similar to our refinement model. RNG-Tr is able to improve the accuracy of a variety of initial parsers on 13 languages from the Universal Dependencies Treebanks and the English and Chinese Penn Treebanks, even improving over the new state-of-the-art results achieved by DepBERT, significantly improving the state-of-the-art for all corpora tested.},
            pdf = {https://publications.idiap.ch/attachments/papers/2020/Mohammadshahi_TACL-2_2020.pdf}
}



crossreferenced publications: 
@ARTICLE{Mohammadshahi_TACL_2020,
         author = {Mohammadshahi, Alireza and Henderson, James},
       keywords = {Natural language processing, NLP, Parsing, Transformer},
       projects = {Idiap},
          title = {Recursive Non-Autoregressive Graph-to-Graph Transformer for Dependency Parsing with Iterative Refinement},
        journal = {Transactions of the Association for Computational Linguistics(under submission)},
           year = {2020},
       abstract = {We propose the Recursive Non-autoregressive Graph-to-graph Transformer architecture (RNG-Tr) 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.  The Graph-to-Graph Transformer architecture of \newcite{mohammadshahi2019graphtograph} has previously been used for autoregressive graph prediction, but here we use it to predict all edges of the graph independently, conditioned on a previous prediction of the same graph.  We demonstrate the power and effectiveness of RNG-Tr on several dependency corpora, using a refinement model pre-trained with BERT~\cite{devlin2018bert}.  We also introduce Dependency BERT (DepBERT), a non-recursive parser similar to our refinement model.  RNG-Tr is able to improve the accuracy of a variety of initial parsers on 13 languages from the Universal Dependencies Treebanks and the English and Chinese Penn Treebanks, even improving over the new state-of-the-art results achieved by DepBERT, significantly improving the state-of-the-art for all corpora tested.}
}