@InProceedings{khwileh-EtAl:2017:W17-13,
  author    = {Khwileh, Ahmad  and  Afli, Haithem  and  Jones, Gareth  and  Way, Andy},
  title     = {Identifying Effective Translations for Cross-lingual Arabic-to-English User-generated Speech Search},
  booktitle = {Proceedings of the Third Arabic Natural Language Processing Workshop},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {100--109},
  abstract  = {Cross Language Information Retrieval (CLIR) systems are a valuable tool to
	enable speakers of one language to search for content of interest expressed in
	a different language. A group for whom this is of particular interest is
	bilingual Arabic speakers who wish to search for English language content using
	information needs expressed in Arabic queries. A key challenge in CLIR is
	crossing the language barrier between the query and the documents. The most
	common approach to bridging this gap is automated query translation, which can
	be unreliable for vague or short queries. In this work, we examine the
	potential for improving CLIR effectiveness by predicting the translation
	effectiveness using Query Performance Prediction (QPP) techniques. We propose a
	novel QPP method to estimate the quality of translation for an Arabic-English
	Cross-lingual User-generated Speech Search (CLUGS) task. We present an
	empirical evaluation that demonstrates the quality of our method on alternative
	translation outputs extracted from an Arabic-to-English Machine Translation
	system developed for this task. Finally, we show how this framework can be
	integrated in CLUGS to find relevant translations for improved retrieval
	performance.},
  url       = {http://www.aclweb.org/anthology/W17-1313}
}