@InProceedings{scarton-paetzold-specia:2016:COLINGTuto,
  author    = {Scarton, Carolina  and  Paetzold, Gustavo  and  Specia, Lucia},
  title     = {Quality Estimation for Language Output Applications},
  booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Tutorial Abstracts},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {14--17},
  abstract  = {Quality Estimation (QE) of language output applications is a research area that
	has been attracting significant attention. The goal of QE is to estimate the
	quality of language output applications without the need of human references.
	Instead, machine learning algorithms are used to build supervised models based
	on a few labelled training instances. Such models are able to generalise over
	unseen data and thus QE is a robust method applicable to scenarios where human
	input is not available or possible. One such a scenario where QE is
	particularly appealing is that of Machine Translation, where a score for
	predicted quality can help decide whether or not a translation is useful (e.g.
	for post-editing) or reliable (e.g. for gisting). Other potential applications
	within Natural Language Processing (NLP) include Text Summarisation and Text
	Simplification. In this tutorial we present the task of QE and its application
	in NLP, focusing on Machine Translation. We also introduce QuEst++, a toolkit
	for QE that encompasses feature extraction and machine learning, and propose a
	practical activity to extend this toolkit in various ways.},
  url       = {http://aclweb.org/anthology/C16-3004}
}