tailieunhanh - Báo cáo khoa học: "Inducing Word Alignments with Bilexical Synchronous Trees"

This paper compares different bilexical tree-based models for bilingual alignment. EM training for the new model benefits from the dynamic programming “hook trick”. The model produces improved dependency structure for both languages. that we must choose how to lexicalize very carefully to control complexity. In this paper we compare two approaches to lexicalization, both of which incorporate bilexical probabilities. | Inducing Word Alignments with Bilexical Synchronous Trees Hao Zhang and Daniel Gildea Computer Science Department University of Rochester Rochester NY 14627 Abstract This paper compares different bilexical tree-based models for bilingual alignment. EM training for the new model benefits from the dynamic programming hook trick . The model produces improved dependency structure for both languages. 1 Introduction A major difficulty in statistical machine translation is the trade-off between representational power and computational complexity. Real-world corpora for language pairs such as Chinese-English have complex reordering relationships that are not captured by current phrase-based MT systems despite their state-of-the-art performance measured in competitive evaluations. Synchronous grammar formalisms that are capable of modeling such complex relationships while maintaining the context-free property in each language have been proposed for many years Aho and Ullman 1972 Wu 1997 Yamada and Knight 2001 Melamed 2003 Chiang 2005 but have not been scaled to large corpora and long sentences until recently. In Synchronous Context Free Grammars there are two sources of complexity grammar branching factor and lexicalization. In this paper we focus on the second issue constraining the grammar to the binary-branching Inversion Transduction Grammar of Wu 1997 . Lexicalization seems likely to help models predict alignment patterns between languages and has been proposed by Melamed 2003 and implemented by Alshawi et al. 2000 and Zhang and Gildea 2005 . However each piece of lexical information considered by a model multiplies the number of states of dynamic programming algorithms for inference meaning that we must choose how to lexicalize very carefully to control complexity. In this paper we compare two approaches to lexicalization both of which incorporate bilexical probabilities. One model uses bilexical probabilities across languages while the other uses bilex-ical .

• Báo cáo khoa học
• Inducing Word Alignments
• Bilexical Synchronous Trees
• Hao Zhang and Daniel Gildea
• 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