TAILIEUCHUNG - Báo cáo khoa học: "Aggregation improves learning: experiments in natural language generation for intelligent tutoring systems"

To improve the interaction between students and an intelligent tutoring system, we developed two Natural Language generators, that we systematically evaluated in a three way comparison that included the original system as well. We found that the generator which intuitively produces the best language does engender the most learning. Specifically, it appears that functional aggregation is responsible for the improvement. | Aggregation improves learning experiments in natural language generation for intelligent tutoring systems Barbara Di Eugenio and Davide Fossati and Dan Yu University of Illinois Chicago IL 60607 USA bdieugen dfossa1 dyu6 @ Susan Haller University of Wisconsin - Parkside Kenosha WI 53141 USA haller@ Abstract To improve the interaction between students and an intelligent tutoring system we developed two Natural Language generators that we systematically evaluated in a three way comparison that included the original system as well. We found that the generator which intuitively produces the best language does engender the most learning. Specifically it appears that functional aggregation is responsible for the improvement. 1 Introduction The work we present in this paper addresses three issues evaluation of Natural Language Generation NLG systems the place of aggregation in NLG and NL interfaces for Intelligent Tutoring Systems. NLG systems have been evaluated in various ways such as via task efficacy measures . measuring how well the users of the system perform on the task at hand Young 1999 Carenini and Moore 2000 Reiter et al. 2003 . We also employed task efficacy as we evaluated the learning that occurs in students interacting with an Intelligent Tutoring System ITS enhanced with NLG capabilities. We focused on sentence planning and specifically on aggregation. We developed two different feedback generation engines that we systematically evaluated in a three way comparison that included the original system as well. Our work is novel for NLG evaluation in that we focus on one specific component of the NLG process aggregation. Aggregation pertains to combining two or more of the messages to be communicated into one sentence Reiter and Dale 2000 . Whereas it is considered an es- Michael Glass Valparaiso University Valparaiso IN 46383 UsA sential task of an NLG system its specific contributions to the effectiveness of the .

• Báo cáo khoa học
• Aggregation improves learning
• experiments in natural language generation
• intelligent tutoring systems
• 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