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State of the art approaches for (embedding based) unsupervised semanticsearch exploits either compositional similarity (of a query and a passage) orpair-wise word (or term) similarity (from the query and the passage). Bydesign, word based approaches do not incorporate similarity in the largercontext (query/passage), while compositional similarity based approaches areusually unable to take advantage of the most important cues in the context. Inthis paper we propose a new compositional similarity based approach, calledvariable centroid vector (VCVB), that tries to address both of theselimitations. We also presents results using a different type of compositionalsimilarity based approach by exploiting universal sentence embedding. Weprovide empirical evaluation on two different benchmarks.

A Study on Passage Re-ranking in Embedding based Unsupervised Semantic Search