Explorer UParse : the Edinburgh system for the CoNLL 2017 UD shared task

@inproceedings{Vania2017ExplorerU,
  title={Explorer UParse : the Edinburgh system for the CoNLL 2017 UD shared task},
  author={Clara Vania and Xingxing Zhang and Adam Lopez},
  year={2017}
}
This paper presents our submissions for the CoNLL 2017 UD Shared Task. Our parser, called UParse, is based on a neural network graph-based dependency parser. The parser uses features from a bidirectional LSTM to produce a distribution over possible heads for each word in the sentence. To allow transfer learning for lowresource treebanks and surprise languages, we train several multilingual models for related languages, grouped by their genus and language families. Out of 33 participants, our… CONTINUE READING