TAILIEUCHUNG - Báo cáo khoa học: "Machine Translation by Triangulation: Making Effective Use of Multi-Parallel Corpora"

Current phrase-based SMT systems perform poorly when using small training sets. This is a consequence of unreliable translation estimates and low coverage over source and target phrases. This paper presents a method which alleviates this problem by exploiting multiple translations of the same source phrase. Central to our approach is triangulation, the process of translating from a source to a target language via an intermediate third language. This allows the use of a much wider range of parallel corpora for training, and can be combined with a standard phrase-table using conventional smoothing methods. . | Machine Translation by Triangulation Making Effective Use of Multi-Parallel Corpora Trevor Cohn and Mirella Lapata Human Computer Research Centre School of Informatics University of Edinburgh tcohn mlap @ Abstract Current phrase-based SMT systems perform poorly when using small training sets. This is a consequence of unreliable translation estimates and low coverage over source and target phrases. This paper presents a method which alleviates this problem by exploiting multiple translations of the same source phrase. Central to our approach is triangulation the process of translating from a source to a target language via an intermediate third language. This allows the use of a much wider range of parallel corpora for training and can be combined with a standard phrase-table using conventional smoothing methods. Experimental results demonstrate BLEU improvements for triangulated models over a standard phrase-based system. 1 Introduction Statistical machine translation Brown et al. 1993 has seen many improvements in recent years most notably the transition from word- to phrase-based models Koehn et al. 2003 . Modern SMT systems are capable of producing high quality translations when provided with large quantities of training data. With only a small training sample the translation output is often inferior to the output from using larger corpora because the translation algorithm must rely on more sparse estimates of phrase frequencies and must also back-off to smaller sized phrases. This often leads to poor choices of target phrases and reduces the coherence of the output. Unfortunately parallel corpora are not readily available in large quantities except for a small subset of the world s languages see Resnik and Smith 2003 for discussion therefore limiting the potential use of current SMT systems. 728 In this paper we provide a means for obtaining more reliable translation frequency estimates from small datasets. We make use of multi-parallel corpora .

• Báo cáo khoa học
• Machine Translation by Triangulation
• Making Effective
• Multi Parallel Corpora
• 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