TAILIEUCHUNG - Báo cáo khoa học: "Multi-Engine Machine Translation Guided by Explicit Word Matching"

We describe a new approach for synthetically combining the output of several different Machine Translation (MT) engines operating on the same input. The goal is to produce a synthetic combination that surpasses all of the original systems in translation quality. Our approach uses the individual MT engines as “black boxes” and does not require any explicit cooperation from the original MT systems. A decoding algorithm uses explicit word matches, in conjunction with confidence estimates for the various engines and a trigram language model in order to score and rank a collection of sentence hypotheses that are synthetic combinations of. | Multi-Engine Machine Translation Guided by Explicit Word Matching Shyamsundar Jayaraman Language Technologies Institute Carnegie Mellon University Pittsburgh PA 15213 shyamj @ Alon Lavie Language Technologies Institute Carnegie Mellon University Pittsburgh PA 15213 alavie @ Abstract We describe a new approach for synthetically combining the output of several different Machine Translation MT engines operating on the same input. The goal is to produce a synthetic combination that surpasses all of the original systems in translation quality. Our approach uses the individual MT engines as black boxes and does not require any explicit cooperation from the original MT systems. A decoding algorithm uses explicit word matches in conjunction with confidence estimates for the various engines and a trigram language model in order to score and rank a collection of sentence hypotheses that are synthetic combinations of words from the various original engines. The highest scoring sentence hypothesis is selected as the final output of our system. Experiments using several Arabic-to-English systems of similar quality show a substantial improvement in the quality of the translation output. 1 Introduction A variety of different paradigms for machine translation MT have been developed over the years ranging from statistical systems that learn mappings between words and phrases in the source language and their corresponding translations in the target language to Interlingua-based systems that perform deep semantic analysis. Each approach and system has different advantages and disadvantages. While statistical systems provide broad coverage with little manpower the quality of the corpus based systems rarely reaches the quality of knowledge based systems. With such a wide range of approaches to machine translation it would be beneficial to have an effective framework for combining these systems into an MT system that carries many of the advantages of the individual

• Báo cáo khoa học
• Multi Engine Machine
• Translation Guided
• Explicit Word Matching
• 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