TAILIEUCHUNG - Báo cáo khoa học: "Better Alignments = Better Translations?"

Automatic word alignment is a key step in training statistical machine translation systems. Despite much recent work on word alignment methods, alignment accuracy increases often produce little or no improvements in machine translation quality. In this work we analyze a recently proposed agreement-constrained EM algorithm for unsupervised alignment models. We attempt to tease apart the effects that this simple but effective modification has on alignment precision and recall trade-offs, and how rare and common words are affected across several language pairs. . | Better Alignments Better Translations Kuzman Ganchev Computer Information Science University of Pennsylvania kuzman@ Joao V. Graca L2f INESC-ID Lisboa Portugal javg@ Ben Taskar Computer Information Science University of Pennsylvania taskar@ Abstract Automatic word alignment is a key step in training statistical machine translation systems. Despite much recent work on word alignment methods alignment accuracy increases often produce little or no improvements in machine translation quality. In this work we analyze a recently proposed agreement-constrained EM algorithm for unsupervised alignment models. We attempt to tease apart the effects that this simple but effective modification has on alignment precision and recall trade-offs and how rare and common words are affected across several language pairs. We propose and extensively evaluate a simple method for using alignment models to produce alignments better-suited for phrase-based MT systems and show significant gains as measured by BLEU score in end-to-end translation systems for six languages pairs used in recent MT competitions. 1 Introduction The typical pipeline for a machine translation MT system starts with a parallel sentence-aligned corpus and proceeds to align the words in every sentence pair. The word alignment problem has received much recent attention but improvements in standard measures of word alignment performance often do not result in better translations. Fraser and Marcu 2007 note that none of the tens of papers published over the last five years has shown that significant decreases in alignment error rate AER result in significant increases in translation performance. In this work we show that by changing the way the word alignment models are trained and used we can get not only improvements in alignment performance but also in the performance of the MT system that uses those alignments. We present extensive experimental results evaluating a new training

• Báo cáo khoa học
• Better Alignments
• Better Translations
• Kuzman Ganchev Jo
• báo cáo khoa học
• báo cáo ngôn ngữ
• xử lý ngôn ngữ tự nhiên
• Better Word Alignments
• Supervised ITG Models
• Aria Haghighi
• BMC Bioinformatics
• Methods comparison
• Inter protein
• Cross species
• Host virus
• Contact prediction
• Protein interaction
• 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 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