@InProceedings{alvamanchego-EtAl:2017:I17-1,
  author    = {Alva-Manchego, Fernando  and  Bingel, Joachim  and  Paetzold, Gustavo  and  Scarton, Carolina  and  Specia, Lucia},
  title     = {Learning How to Simplify From Explicit Labeling of Complex-Simplified Text Pairs},
  booktitle = {Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)},
  month     = {November},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Asian Federation of Natural Language Processing},
  pages     = {295--305},
  abstract  = {Current research in text simplification has been hampered by two central
	problems: (i) the small amount of high-quality parallel simplification data
	available, and (ii) the lack of explicit annotations of simplification
	operations, such as deletions or substitutions, on existing data. While the
	recently introduced Newsela corpus has alleviated the first problem,
	simplifications still need to be learned directly from parallel text using
	black-box, end-to-end approaches rather than from explicit annotations. These
	complex-simple parallel sentence pairs often differ to such a high degree that
	generalization becomes difficult.  End-to-end models also make it hard to
	interpret what is actually learned from data. We propose a method that
	decomposes the task of TS into its sub-problems. We devise a way to
	automatically identify operations in a parallel corpus and introduce a
	sequence-labeling approach based on these annotations. Finally, we provide
	insights on the types of transformations that different approaches can model.},
  url       = {http://www.aclweb.org/anthology/I17-1030}
}