We explore the potential of probabilistic topic modeling within the relevance modeling framework for both monolingual and cross-lingual ad-hoc retrieval. Multilingual topic models provide a way to represent documents in a structured and coherent way, regardless of their actual language, by means of language-independent concepts, that is, cross-lingual topics. We show how to integrate the topical knowledge into a unified relevance modeling framework in order to build quality retrieval models in monolingual and cross-lingual contexts. The proposed modeling framework processes all documents uniformly and does not make any conceptual distinction between monolingual and cross-lingual modeling. Our results obtained from the experiments conducted on the standard CLEF test collections reveal that fusing the topical knowledge and relevance modeling leads to building monolingual and cross-lingual retrieval models that outperform several strong baselines. We show that that the topical knowledge coming from a general Web-generated corpus boosts retrieval scores. Additionally, we show that within this framework the estimation of cross-lingual relevance models may be performed by exploiting only a general non-parallel corpus.
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