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Cache-based Document-level Statistical Machine Translation
Conference on Empirical Methods in Natural Language Processing
Statistical machine translation systems are usually trained on a large amount of bilingual sentence pairs and translate one sentence at a time, ignoring document-level information. In this paper, we propose a cache-based approach to document-level translation. Since caches mainly depend on relevant data to supervise subsequent decisions, it is critical to fill the caches with highly-relevant data of a reasonable size. In this paper, we present three kinds of caches to store relevantdblp:conf/emnlp/GongZZ11 fatcat:tavbosbfwbe7xjh75rr44bnyom