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    題名: Statistical approaches to patent translation - Experiments with various settings of training data
    作者: Tseng, Yuen-Hsien;Liu, Chao-Lin;Tsai, Chia-Chi;Wang, Jui-Ping;Chuang, Yi-Hsuan;Jeng, James
    劉昭麟
    貢獻者: 資科系
    關鍵詞: Chinese segmentation, language modeling, training corpus
    日期: 2011-12
    上傳時間: 2016-06-22 17:10:06 (UTC+8)
    摘要: This paper describes our experiments and results in the NTCIR-9 Chinese-to-English Patent Translation Task. A series of open source software were integrated to build a statistical machine translation model for the task. Various Chinese segmentation, additional resources, and training corpus preprocessing were then tried based on this model. As a result, more than 20 experiments were conducted to compare the translation performance. Our current results show that 1) consistent segmentation between the training and testing data is important to maintain the performance; 2) sufficient number of good quality bilingual training sentences is more helpful than additional bilingual dictionaries; and 3) the translation effectiveness in BLEU values doubles as the number of bilingual training sentences at the level of 100,000 doubles.
    關聯: Proceedings of the Ninth NTCIR Workshop Meeting on Evaluation of Information Access Technologies: Information Retrieval, Question Answering and Cross-Lingual Information Access - PatentMT (NTCIR 9), 661‒665. Tokyo, Japan, 6-9 December 2011
    資料類型: conference
    顯示於類別:[資訊科學系] 會議論文

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