tailieunhanh - Báo cáo khoa học: "A Polynomial-Time Algorithm for Statistical Machine Translation"

We introduce a polynomial-time algorithm for statistical machine translation. This algorithm can be used in place of the expensive, slow best-first search strategies in current statistical translation architectures. The approach employs the stochastic bracketing transduction grammar (SBTG) model we recently introduced to replace earlier word alignment channel models, while retaining a bigram language model. The new algorithm in our experience yields major speed improvement with no significant loss of accuracy. . | A Polynomial-Time Algorithm for Statistical Machine Translation Dekai Wu HKUST Department of Computer Science University of Science and Technology Clear Water Bay Hong Kong Abstract We introduce a polynomial-time algorithm for statistical machine translation. This algorithm can be used in place of the expensive slow best-first search strategies in current statistical translation architectures. The approach employs the stochastic bracketing transduction grammar SBTG model we recently introduced to replace earlier word alignment channel models while retaining a bigram language model. The new algorithm in our experience yields major speed improvement with no significant loss of accuracy. 1 Motivation The statistical translation model introduced by IBM Brown et al. 1990 views translation as a noisy channel process. Assume as we do throughout this paper that the input language is Chinese and the task is to translate into English. The underlying generative model shown in Figure 1 contains a stochastic English sentence generator whose output is corrupted by the translation channel to produce Chinese sentences. In the IBM system the language model employs simple n-grams while the translation model employs several sets of parameters as discussed below. Estimation of the parameters has been described elsewhere Brown et al. 1993 . Translation is performed in the reverse direction from generation as usual for recognition under generative models. For each Chinese sentence c that is to be translated the system must attempt to find the English sentence e such that 1 e argmaxPr eịc e 2 argmaxPr c e Pr e e In the IBM model the search for the optimal e is performed using a best-first heuristic stack search similar to A methods. One of the primary obstacles to making the statistical translation approach practical is slow speed of translation as performed in A fashion. This price is paid for the robustness that is obtained by using very flexible language and .

• Báo cáo khoa học
• A Polynomial Time Algorithm
• Statistical Machine Translation
• Dekai Wu
• 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