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Inf. Syst."],"published-print":{"date-parts":[[2014,10,28]]},"abstract":"<jats:p>\n            This work explores how internal representations of modern statistical machine translation systems can be exploited for cross-language information retrieval. We tackle two core issues that are central to query translation: how to exploit context to generate more accurate translations and how to preserve ambiguity that may be present in the original query, thereby retaining a diverse set of translation alternatives. These two considerations are often in tension since ambiguity in natural language is typically resolved by exploiting context, but effective retrieval requires striking the right balance. We propose two novel query translation approaches: the\n            <jats:italic>grammar-based<\/jats:italic>\n            approach extracts translation probabilities from translation grammars, while the\n            <jats:italic>decoder-based<\/jats:italic>\n            approach takes advantage of\n            <jats:italic>n<\/jats:italic>\n            -best translation hypotheses. Both are\n            <jats:italic>context-sensitive<\/jats:italic>\n            , in contrast to a baseline\n            <jats:italic>context-insensitive<\/jats:italic>\n            approach that uses bilingual dictionaries for word-by-word translation. Experimental results show that by \u201copening up\u201d modern statistical machine translation systems, we can access intermediate representations that yield high retrieval effectiveness. By combining evidence from multiple sources, we demonstrate significant improvements over competitive baselines on standard cross-language information retrieval test collections. In addition to effectiveness, the efficiency of our techniques are explored as well.\n          <\/jats:p>","DOI":"10.1145\/2644807","type":"journal-article","created":{"date-parts":[[2014,10,29]],"date-time":"2014-10-29T18:05:55Z","timestamp":1414605955000},"page":"1-32","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["Exploiting Representations from Statistical Machine Translation for Cross-Language Information Retrieval"],"prefix":"10.1145","volume":"32","author":[{"given":"Ferhan","family":"Ture","sequence":"first","affiliation":[{"name":"Raytheon BBN Technologies, Cambridge, MA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jimmy","family":"Lin","sequence":"additional","affiliation":[{"name":"University of Maryland at College Park"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2014,10,28]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Proceedings of RIAO: Content-Based Multimedia Information Access.","author":"Adriani M.","year":"2000","unstructured":"M. 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