TAILIEUCHUNG - Báo cáo khoa học: "Improved Word-Level System Combination for Machine Translation"

Recently, confusion network decoding has been applied in machine translation system combination. Due to errors in the hypothesis alignment, decoding may result in ungrammatical combination outputs. This paper describes an improved confusion network based method to combine outputs from multiple MT systems. In this approach, arbitrary features may be added log-linearly into the objective function, thus allowing language model expansion and re-scoring. Also, a novel method to automatically select the hypothesis which other hypotheses are aligned against is proposed. . | Improved Word-Level System Combination for Machine Translation Antti-Veikko I. Rosti and Spyros Matsoukas and Richard Schwartz BBN Technologies 10 Moulton Street Cambridge MA 02138 arosti smatsouk schwartz @ Abstract Recently confusion network decoding has been applied in machine translation system combination. Due to errors in the hypothesis alignment decoding may result in ungrammatical combination outputs. This paper describes an improved confusion network based method to combine outputs from multiple MT systems. In this approach arbitrary features may be added log-linearly into the objective function thus allowing language model expansion and re-scoring. Also a novel method to automatically select the hypothesis which other hypotheses are aligned against is proposed. A generic weight tuning algorithm may be used to optimize various automatic evaluation metrics including TER BLEU and METEOR. The experiments using the 2005 Arabic to English and Chinese to English NIST MT evaluation tasks show significant improvements in BLEU scores compared to earlier confusion network decoding based methods. 1 Introduction System combination has been shown to improve classification performance in various tasks. There are several approaches for combining classifiers. In ensemble learning a collection of simple classifiers is used to yield better performance than any single classifier for example boosting Schapire 1990 . Another approach is to combine outputs from a few highly specialized classifiers. The classifiers may 312 be based on the same basic modeling techniques but differ by for example alternative feature representations. Combination of speech recognition outputs is an example of this approach Fiscus 1997 . In speech recognition confusion network decoding Mangu et al. 2000 has become widely used in system combination. Unlike speech recognition current statistical machine translation MT systems are based on various different paradigms for example phrasal .

• Báo cáo khoa học
• Improved Word Level
• System Combination
• Machine Translation
• 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