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-Engish 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