TAILIEUCHUNG - Báo cáo khoa học: "Variational Decoding for Statistical Machine Translation"

Statistical models in machine translation exhibit spurious ambiguity. That is, the probability of an output string is split among many distinct derivations (., trees or segmentations). In principle, the goodness of a string is measured by the total probability of its many derivations. However, finding the best string (., during decoding) is then computationally intractable. Therefore, most systems use a simple Viterbi approximation that measures the goodness of a string using only its most probable derivation. Instead, we develop a variational approximation, which considers all the derivations but still allows tractable decoding. . | Variational Decoding for Statistical Machine Translation Zhifei Li and Jason Eisner and Sanjeev Khudanpur Department of Computer Science and Center for Language and Speech Processing Johns Hopkins University Baltimore MD 21218 USA jason@ khudanpur@ Abstract Statistical models in machine translation exhibit spurious ambiguity. That is the probability of an output string is split among many distinct derivations . trees or segmentations . In principle the goodness of a string is measured by the total probability of its many derivations. However finding the best string . during decoding is then computationally intractable. Therefore most systems use a simple Viterbi approximation that measures the goodness of a string using only its most probable derivation. Instead we develop a variational approximation which considers all the derivations but still allows tractable decoding. Our particular variational distributions are parameterized as n-gram models. We also analytically show that interpolating these n-gram models for different n is similar to minimumrisk decoding for BLEU Tromble et al. 2008 . Experiments show that our approach improves the state of the art. 1 Introduction Ambiguity is a central issue in natural language processing. Many systems try to resolve ambiguities in the input for example by tagging words with their senses or choosing a particular syntax tree for a sentence. These systems are designed to recover the values of interesting latent variables such as word senses syntax trees or translations given the observed input. However some systems resolve too many ambiguities. They recover additional latent variables so-called nuisance variables that are not of interest to the For example though machine translation MT seeks to output a string typical MT systems Koehn et al. 2003 Chiang 2007 1These nuisance variables may be annotated in training data but it is more common for them to be latent even there .

• Báo cáo khoa học
• Variational Decoding
• Statistical Machine Translation
• Zhifei Li and Jason Eisner and Sanjeev Khudanpur
• báo cáo khoa học
• báo cáo 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 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