TAILIEUCHUNG - Báo cáo khoa học: "Interactively Exploring a Machine Translation Model"

This paper describes a method of interactively visualizing and directing the process of translating a sentence. The method allows a user to explore a model of syntax-based statistical machine translation (MT), to understand the model’s strengths and weaknesses, and to compare it to other MT systems. Using this visualization method, we can find and address conceptual and practical problems in an MT system. In our demonstration at ACL, new users of our tool will drive a syntaxbased decoder for themselves. . | Interactively Exploring a Machine Translation Model Steve DeNeefe Kevin Knight and Hayward H. Chan Information Sciences Institute and Department of Computer Science The Viterbi School of Engineering University of Southern California 4676 Admiralty Way Suite 1001 Marina del Rey Ca 90292 sdeneefe knight @ hhchan@ Abstract This paper describes a method of interactively visualizing and directing the process of translating a sentence. The method allows a user to explore a model of syntax-based statistical machine translation MT to understand the model s strengths and weaknesses and to compare it to other MT systems. Using this visualization method we can find and address conceptual and practical problems in an MT system. In our demonstration at ACL new users of our tool will drive a syntaxbased decoder for themselves. 1 Introduction There are many new approaches to statistical machine translation and more ideas are being suggested all the time. However it is difficult to determine how well a model will actually perform. Experienced researchers have been surprised by the capability of unintuitive word-for-word models at the same time seemingly capable models often have serious hidden problems intuition is no substitute for experimentation. With translation ideas growing more complex capturing aspects of linguistic structure in different ways it becomes difficult to try out a new idea without a large-scale software development effort. Anyone who builds a full-scale trainable translation system using syntactic information faces this problem. We know that syntactic models often do not fit the data. For example the syntactic system described in Yamada and Knight 2001 cannot translate n-to-m-word phrases and does not allow for multi-level syntactic transformations both phenomena are frequently observed in real data. In building a new syntax-based MT system which addresses these flaws we wanted to find problems in our framework as early as possible. So we .

• Báo cáo khoa học
• Interactively Exploring
• Machine Translation Model
• Steve DeNeefe
• Kevin Knight
• and Hayward H
• Chan
• 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