@InProceedings{inoue-shindo-matsumoto:2017:CoNLL,
  author    = {Inoue, Go  and  Shindo, Hiroyuki  and  Matsumoto, Yuji},
  title     = {Joint Prediction of Morphosyntactic Categories for Fine-Grained Arabic Part-of-Speech Tagging Exploiting Tag Dictionary Information},
  booktitle = {Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {421--431},
  abstract  = {Part-of-speech (POS) tagging for morphologically rich languages such as Arabic
	is a challenging problem because of their enormous tag sets. One reason for
	this is that in the tagging scheme for such languages, a complete POS tag is
	formed by combining tags from multiple tag sets defined for each
	morphosyntactic category. Previous approaches in Arabic POS tagging applied one
	model for each morphosyntactic tagging task, without utilizing shared
	information between the tasks. In this paper, we propose an approach that
	utilizes this information by jointly modeling multiple morphosyntactic tagging
	tasks with a multi-task learning framework. We also propose a method of
	incorporating tag dictionary information into our neural models by combining
	word representations with representations of the sets of possible tags. Our
	experiments showed that the joint model with tag dictionary information results
	in an accuracy of 91.38% on the Penn Arabic Treebank data set, with an absolute
	improvement of 2.11% over the current state-of-the-art tagger.},
  url       = {http://aclweb.org/anthology/K17-1042}
}