Improving Patent Translation using Bilingual Term Extraction and Re-tokenization for Chinese-Japanese
نویسندگان
چکیده
Unlike European languages, many Asian languages like Chinese and Japanese do not have typographic boundaries in written system. Word segmentation (tokenization) that break sentences down into individual words (tokens) is normally treated as the first step for machine translation (MT). For Chinese and Japanese, different rules and segmentation tools lead different segmentation results in different level of granularity between Chinese and Japanese. To improve the translation accuracy, we adjust and balance the granularity of segmentation results around terms for Chinese–Japanese patent corpus for training translation model. In this paper, we describe a statistical machine translation (SMT) system which is built on re-tokenized Chinese–Japanese patent training corpus using extracted bilingual multi-word terms.
منابع مشابه
Bilingual Multi-Word Term Tokenization for Chinese–Japanese Patent Translation
We propose to re-tokenize data with aligned bilingual multi-word terms to improve statistical machine translation (SMT) in technical domains. For that, we independently extract multi-word terms from the monolingual parts of the training data. Promising bilingual multi-word terms are then identified using the sampling-based alignment method by setting some threshold on translation probabilities....
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