TAILIEUCHUNG - Báo cáo khoa học: "EXPERIMENTS AND PROSPECTS OF EXAMPLE-BASED MACHINE TRANSLATION"

EBMT has the following features: (1) It is easily upgraded simply by inputting appropriate examples to the database; (2) It assigns a reliability factcr to the translation result; (3) It is acoelerated effectively by both indexing and parallel computing; (4) It is robust because of best-match reasoning; ~ d (5) It well utilizes translator expertise. A prototype system has been implemented to deal with a difficult translation problem for conventional Rule-Based Machine Translation (RBMT), ., translating Japanese noun phrases of the form "N~ no N2" into English. The system has achieved about a 78% success rate on average. . | EXPERIMENTS AND PROSPECTS OF EXAMPLE-BASED MACHINE TRANSLATION Eiichiro SUMITA and________ Hitoshi nDA ATR Interpreting Telephony Research Laboratories Sanpeidani Inuidani Seika-cho Souraku-gun Kyoto 619 02 JAPAN ABSTRACT EBMT Example-Based Machine Translation is proposed. EBMT retrieves similar examples pairs of source phrases sentences or texts and their translations from a database of examples adapting the examples to translate a new input. EBMT has the following features 1 It is easily upgraded simply by inputting appropriate examples to die database 2 It assigns a reliability factor to the translation result 3 It is accelerated effectively by both indexing and parallel computing 4 It is robust because of best-match reasoning and 5 It well utilizes translator expertise. A prototype system has been implemented to deal with a difficult translation problem for conventional Rule-Based Machine Translation RBMT . translating Japanese noun phrases of the form Nị no N2 into English. The system has achieved about a 78 success rate on average. This paper explains the basic idea of EBMT illustrates the experiment in detail explains the broad applicability of EBMT to several difficult ữanslation problems for RBMT and discusses the advantages of integrating EBMT with RBMT. 1 INTRODUCTION Machine Translation requires handcrafted and complicated large-scale knowledge Nirenburg 1987 . Conventional machine translation systems use rules as the knowledge. This framework is called Rule-Based Machine Translation RBMT . It is difficult to scale up from a toy program to a practical system because of the problem of building such a large-scale rule-base. It is also difficult to improve translation performance because the effect of adding a new rule is hard to anticipate and because translation using a large-scale rule-based system is time-consuming. Moreover it is difficult to make use of situational or domain-specific information for translation. In order to conquer these problems

• Báo cáo khoa học
• EXPERIMENTS AND PROSPECTS
• EXAMPLE BASED MACHINE TRANSLATION
• Eiichiro SUMITA and Hitoshi HDA
• 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