tailieunhanh - Báo cáo khoa học: "Fast Decoding and Optimal Decoding for Machine Translation"

A good decoding algorithm is critical to the success of any statistical machine translation system. The decoder’s job is to find the translation that is most likely according to set of previously learned parameters (and a formula for combining them). Since the space of possible translations is extremely large, typical decoding algorithms are only able to examine a portion of it, thus risking to miss good solutions. In this paper, we compare the speed and output quality of a traditional stack-based decoding algorithm with two new decoders: a fast greedy decoder and a slow but optimal decoder that treats. | Fast Decoding and Optimal Decoding for Machine Translation Ulrich Germannf Michael JahrJ Kevin Knightf Daniel Marcuf and Kenji Yamadaf f Information Sciences Institute University of Southern California 4676 Admiralty Way Suite 1001 Marina del Rey CA 90292 germann knight marcu kyamada @ ỊDepartment of Computer Science Stanford University Stanford CA 94305 jahr@ Abstract A good decoding algorithm is critical to the success of any statistical machine translation system. The decoder s job is to find the translation that is most likely according to set of previously learned parameters and a formula for combining them . Since the space of possible translations is extremely large typical decoding algorithms are only able to examine a portion of it thus risking to miss good solutions. In this paper we compare the speed and output quality of a traditional stack-based decoding algorithm with two new decoders a fast greedy decoder and a slow but optimal decoder that treats decoding as an integer-programming optimization problem. 1 Introduction A statistical MT system that translates say French sentences into English is divided into three parts 1 a language model LM that assigns a probability P e to any English string 2 a translation model TM that assigns a probability P f e to any pair of English and French strings and 3 a decoder. The decoder takes a previously unseen sentence J and tries to find the e that maximizes P e f or equivalently maximizes P e -P fe . Brown et al. 1993 introduced a series of TMs based on word-for-word substitution and reordering but did not include a decoding algorithm. If the source and target languages are constrained to have the same word order by choice or through suitable pre-processing then the linear Viterbi algorithm can be applied Tillmann et al. 1997 . If re-ordering is limited to rotations around nodes in a binary tree then optimal decoding can be carried out by a high-polynomial algorithm Wu 1996 . For arbitrary .

• Báo cáo khoa học
• Fast Decoding
• Optimal Decoding
• Machine Translation
• báo cáo khoa học
• mô hình ngôn ngữ
• xử lý ngôn ngữ tự nhiên
• Fast and Scalable Decoding
• Language Model Look Ahead
• Phrase based Statistical Machine Translation
• báo cáo ngôn ngữ
• ngôn ngữ tự nhiên
• Jointly optimizing a two step conditional random field model
• machine transliteration
• fast decoding algorithm
• Fast Consensus Decoding
• Translation Forests
• John DeNero David Chiang and Kevin Knight
• Bài giảng Xử lý 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