TAILIEUCHUNG - Báo cáo khoa học: "Forest Rescoring: Faster Decoding with Integrated Language Models ∗"

Efficient decoding has been a fundamental problem in machine translation, especially with an integrated language model which is essential for achieving good translation quality. We develop faster approaches for this problem based on k-best parsing algorithms and demonstrate their effectiveness on both phrase-based and syntax-based MT systems. In both cases, our methods achieve significant speed improvements, often by more than a factor of ten, over the conventional beam-search method at the same levels of search error and translation accuracy. . | Forest Rescoring Faster Decoding with Integrated Language Models Liang Huang University of Pennsylvania Philadelphia PA 19104 lhuang3@ David Chiang USC Information Sciences Institute Marina del Rey CA 90292 chiang@ Abstract Efficient decoding has been a fundamental problem in machine translation especially with an integrated language model which is essential for achieving good translation quality. We develop faster approaches for this problem based on k-best parsing algorithms and demonstrate their effectiveness on both phrase-based and syntax-based MT systems. In both cases our methods achieve significant speed improvements often by more than a factor of ten over the conventional beam-search method at the same levels of search error and translation accuracy. 1 Introduction Recent efforts in statistical machine translation MT have seen promising improvements in output quality especially the phrase-based models Och and Ney 2004 and syntax-based models Chiang 2005 Galley et al. 2006 . However efficient decoding under these paradigms especially with integrated language models LMs remains a difficult problem. Part of the complexity arises from the expressive power of the translation model for example a phrase- or word-based model with full reordering has exponential complexity Knight 1999 . The language model also if fully integrated into the decoder introduces an expensive overhead for maintaining target-language boundary words for dynamic The authors would like to thank Dan Gildea Jonathan Graehl Mark Johnson Kevin Knight Daniel Marcu Bob Moore and Hao Zhang. L. H. was partially supported by NSF ITR grants IIS-0428020 while visiting USC ISI and EIA-0205456 at UPenn. D. C. was partially supported under the GALE DARPA program contract HR0011-06-C-0022. 144 programming Wu 1996 Och and Ney 2004 . In practice one must prune the search space aggressively to reduce it to a reasonable size. A much simpler alternative method to incorporate the LM is .

• Báo cáo khoa học
• Forest Rescoring
• Faster Decoding
• Integrated Language Models
• báo cáo khoa học
• mô hình ngôn ngữ
• xử lý ngôn ngữ tự nhiên
• Bài giảng Xử lý ngôn ngữ tự nhiên
• Ngôn ngữ tự nhiên
• Kỹ thuật lập trình
• Xử lý nhập nhằng ngữ nghĩa
• Xử lý ngữ nghĩa
• Xử lý ngôn ngữ
• Natural Language Processing
• Ngôn ngữ lập trình
• Nhập nhằng cấu trúc
• Tri thức về ngôn ngữ
• Mô hình n gram
• Dịch máy
• Phương pháp dịch máy
• Hiểu ngôn ngữ
• Phân tích ngữ nghĩa
• Phân loại tin tự động
• Biểu diễn vị từ
• Thuộc tính về sự kiện
• Thuộc tính về sự kiện
• Mô hình ngôn ngữ Google Book N grams
• Mô hình ngôn ngữ KenLM
• Phương pháp làm mịn
• Tách từ tiếng Việt
• Gán nhãn từ loại
• Phân tích cú pháp
• Nghĩa từ vựng
• Phân giải nhập nhằng từ
• Phân loại văn bản
• Lexical chain
• Phản hồi thông tin
• Bài toán PTCP
• Cấu trúc ngữ pháp
• Phân tích cú pháp xác suất
• CKY kết hợp xác suất
• Văn phạm phi ngữ cảnh xác suất
• Hình thái học