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arXiv · 1804.08057

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

Md Faisal Mahbub Chowdhury·Vijil Chenthamarakshan·Rishav Chakravarti·Alfio M. Gliozzo
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Abstract

State of the art approaches for (embedding based) unsupervised semantic search exploits either compositional similarity (of a query and a passage) or pair-wise word (or term) similarity (from the query and the passage). By design, word based approaches do not incorporate similarity in the larger context (query/passage), while compositional similarity based approaches are usually unable to take advantage of the most important cues in the context. In this paper we propose a new compositional similarity based approach, called variable centroid vector (VCVB), that tries to address both of these limitations. We also presents results using a different type of compositional similarity based approach by exploiting universal sentence embedding. We provide empirical evaluation on two different benchmarks.

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Md Faisal Mahbub Chowdhury, Vijil Chenthamarakshan, Rishav Chakravarti, Alfio M. Gliozzo. 2019-03-13. A Study on Passage Re-ranking in Embedding based Unsupervised Semantic Search. https://arxiv.org/abs/1804.08057

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