{"id":468903,"date":"2018-02-23T08:49:40","date_gmt":"2018-02-23T16:49:40","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&#038;p=468903"},"modified":"2018-10-16T22:29:38","modified_gmt":"2018-10-17T05:29:38","slug":"phrase-table-without-phrases-rank-encoding-better-phrase-table-compression","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/phrase-table-without-phrases-rank-encoding-better-phrase-table-compression\/","title":{"rendered":"A phrase table without phrases: Rank encoding for better phrase table Compression"},"content":{"rendered":"<div>This paper describes the first steps towards a minimum-size phrase table implementation to be used for phrase-based statistical machine translation. The focus lies on the size reduction of target language data in a phrase table. Rank Encoding (R-Enc), a novel method for the compression of word-aligned target language in phrase tables is presented. Combined with Huffman coding a relative size reduction of 56 percent for target phrase words and alignment data is achieved when compared to bare Huffman coding without R-Enc. In the context of the complete phrase table the size reduction is 22 percent.<\/div>\n","protected":false},"excerpt":{"rendered":"<p>This paper describes the first steps towards a minimum-size phrase table implementation to be used for phrase-based statistical machine translation. The focus lies on the size reduction of target language data in a phrase table. Rank Encoding (R-Enc), a novel method for the compression of word-aligned target language in phrase tables is presented. Combined with [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13556],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-468903","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-artificial-intelligence","msr-locale-en_us"],"msr_publishername":"","msr_edition":"Proceedings of the 16th Annual Conference of the European Association for Machine 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