tailieunhanh - Báo cáo khoa học: "Semi-Supervised Training for Statistical Word Alignment"

We introduce a semi-supervised approach to training for statistical machine translation that alternates the traditional Expectation Maximization step that is applied on a large training corpus with a discriminative step aimed at increasing word-alignment quality on a small, manually word-aligned sub-corpus. We show that our algorithm leads not only to improved alignments but also to machine translation outputs of higher quality. | Semi-Supervised Training for Statistical Word Alignment Alexander Fraser ISI University of Southern California 4676 Admiralty Way Suite 1001 Marina del Rey CA 90292 fraser@ Daniel Marcu ISI University of Southern California 4676 Admiralty Way Suite 1001 Marina del Rey CA 90292 marcu@ Abstract We introduce a semi-supervised approach to training for statistical machine translation that alternates the traditional Expectation Maximization step that is applied on a large training corpus with a discriminative step aimed at increasing word-alignment quality on a small manually word-aligned sub-corpus. We show that our algorithm leads not only to improved alignments but also to machine translation outputs of higher quality. 1 Introduction The most widely applied training procedure for statistical machine translation IBM model 4 Brown et al. 1993 unsupervised training followed by post-processing with symmetrization heuristics Och and Ney 2003 yields low quality word alignments. When compared with gold standard parallel data which was manually aligned using a high-recall precision methodology Melamed 1998 the word-level alignments produced automatically have an F-measure accuracy of and see Section 2 for details . In this paper we improve word alignment and subsequently MT accuracy by developing a range of increasingly sophisticated methods 1. We first recast the problem of estimating the IBM models Brown et al. 1993 in a discriminative framework which leads to an initial increase in word-alignment accuracy. 2. We extend the IBM models with new sub models which leads to additional increases in word-alignment accuracy. In the process we also show that these improvements are explained not only by the power of the new models but also by a novel search procedure for the alignment of highest probability. 3. Finally we propose a training procedure that interleaves discriminative training with maximum likelihood training. These steps lead to word alignments

• Báo cáo khoa học
• Semi Supervised Training
• Statistical Word Alignment
• Alexander Fraser
• 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