A translation model for sentence retrieval

In this work we propose a translation model for monolingual sentence retrieval. We propose four methods for constructing a parallel corpus. Of the four methods proposed, a lexicon learned from a bilingual Arabic-English corpus aligned at the sentence level performs best, significantly improving results over the query likelihood baseline. Further, we demonstrate that smoothing from the local context of the sentence improves retrieval over the query likelihood baseline.