@InProceedings{kohita-noji-matsumoto:2018:C18-1,
  author    = {Kohita, Ryosuke  and  Noji, Hiroshi  and  Matsumoto, Yuji},
  title     = {Dynamic Feature Selection with Attention in Incremental Parsing},
  booktitle = {Proceedings of the 27th International Conference on Computational Linguistics},
  month     = {August},
  year      = {2018},
  address   = {Santa Fe, New Mexico, USA},
  publisher = {Association for Computational Linguistics},
  pages     = {785--794},
  abstract  = {One main challenge for incremental transition-based parsers, when future inputs are invisible, is to extract good features from a limited local context. In this work, we present a simple technique to maximally utilize the local features with an attention mechanism, which works as context- dependent dynamic feature selection. Our model learns, for example, which tokens should a parser focus on, to decide the next action. Our multilingual experiment shows its effectiveness across many languages. We also present an experiment with augmented test dataset and demon- strate it helps to understand the model’s behavior on locally ambiguous points.},
  url       = {http://www.aclweb.org/anthology/C18-1067}
}