Learning Translational and Knowledge-based Similarities from Relevance Rankings for Cross-Language Retrieval
Shigehiko Schamoni, Felix Hieber, Artem Sokolov, Stefan Riezler · 2014
We present an approach to cross-language retrieval that combines dense knowledgebased features and sparse word translations.Both feature types are learned directly from relevance rankings of bilingual documents in a pairwise ranking framework.In large-scale experiments for patent prior art search and cross-lingual retrieval in Wikipedia, our approach yields considerable improvements over learningto-rank with either only dense or only sparse features, and over very competitive baselines that combine state-of-the-art machine translation and retrieval.