In this paper, we present a novel approach to Pseudo-Relevance Feedback (PRF) called Multilingual PRF (MultiPRF). The key idea is to harness multilinguality. Given a query in a language, we take the help of another language to ameliorate the well known problems of PRF, viz. (a) The expansion terms from PRF are primarily based on co-occurrence relationships with query terms, and thus other terms which are lexically and semantically related, such as morphological variants and synonyms, are not explicitly captured, and (b) PRF is quite sensitive to the quality of the initially retrieved top k documents and is thus not robust. In MultiPRF, given a query in language L1, it is translated into language L2 and PRF is performed on a collection in language L2 and the re