TAILIEUCHUNG - Báo cáo khoa học: "Efficient Multi-pass Decoding for Synchronous Context Free Grammars"

We take a multi-pass approach to machine translation decoding when using synchronous context-free grammars as the translation model and n-gram language models: the first pass uses a bigram language model, and the resulting parse forest is used in the second pass to guide search with a trigram language model. The trigram pass closes most of the performance gap between a bigram decoder and a much slower trigram decoder, but takes time that is insignificant in comparison to the bigram pass. An additional fast decoding pass maximizing the expected count of correct translation hypotheses increases the BLEU score significantly. . | Efficient Multi-pass Decoding for Synchronous Context Free Grammars Hao Zhang and Daniel Gildea Computer Science Department University of Rochester Rochester NY 14627 Abstract We take a multi-pass approach to machine translation decoding when using synchronous context-free grammars as the translation model and n-gram language models the first pass uses a bigram language model and the resulting parse forest is used in the second pass to guide search with a trigram language model. The trigram pass closes most of the performance gap between a bigram decoder and a much slower trigram decoder but takes time that is insignificant in comparison to the bigram pass. An additional fast decoding pass maximizing the expected count of correct translation hypotheses increases the BLEU score significantly. 1 Introduction Statistical machine translation systems based on synchronous grammars have recently shown great promise but one stumbling block to their widespread adoption is that the decoding or search problem during translation is more computationally demanding than in phrase-based systems. This complexity arises from the interaction of the tree-based translation model with an n-gram language model. Use of longer n-grams improves translation results but exacerbates this interaction. In this paper we present three techniques for attacking this problem in order to obtain fast high-quality decoders. First we present a two-pass decoding algorithm in which the first pass explores states resulting from an integrated bigram language model and the second pass expands these states into trigram-based states. The general bigram-to-trigram technique is common in speech recognition Murveit et al. 1993 where lattices from a bigram-based decoder are re-scored with a trigram language model. We examine the question of whether given the reordering inherent in the machine translation problem lower order n-grams will provide as valuable a search heuristic as they do for speech recognition. .

• Báo cáo khoa học
• Efficient Multi pass Decoding
• Synchronous Context Free Grammars
• Hao Zhang and Daniel Gildea
• 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