@inproceedings{khwileh-etal-2017-identifying,
    title = "Identifying Effective Translations for Cross-lingual {A}rabic-to-{E}nglish User-generated Speech Search",
    author = "Khwileh, Ahmad  and
      Afli, Haithem  and
      Jones, Gareth  and
      Way, Andy",
    editor = "Habash, Nizar  and
      Diab, Mona  and
      Darwish, Kareem  and
      El-Hajj, Wassim  and
      Al-Khalifa, Hend  and
      Bouamor, Houda  and
      Tomeh, Nadi  and
      El-Haj, Mahmoud  and
      Zaghouani, Wajdi",
    booktitle = "Proceedings of the Third {A}rabic Natural Language Processing Workshop",
    month = apr,
    year = "2017",
    address = "Valencia, Spain",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/iwcs-25-ingestion/W17-1313/",
    doi = "10.18653/v1/W17-1313",
    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."
}