TAILIEUCHUNG - Báo cáo khoa học: "Learning Optimal Dialogue Strategies: A Case Study of a Spoken Dialogue Agent for Email"

This paper describes a novel method by which a dialogue agent can learn to choose an optimal dialogue strategy. While it is widely agreed that dialogue strategies should be formulated in terms of communicative intentions, there has been little work on automatically optimizing an agent's choices when there are multiple ways to realize a communicative intention. Our method is based on a combination of learning algorithms and empirical evaluation techniques. | Learning Optimal Dialogue Strategies A Case Study of a Spoken Dialogue Agent for Email Marilyn A. Walker walker@ ATT Labs Research 180 Park Ave. Florham Park NJ 07932 Jeanne c. Fromer jeannie@ MIT Al Lab 545 Technology Square Cambridge MA 02139 Shrikanth Narayanan shri @ ATT Labs Research 180 Park Ave. Florham Park NJ 07932 Abstract This paper describes a novel method by which a dialogue agent can learn to choose an optimal dialogue strategy. While it is widely agreed that dialogue strategies should be formulated in terms of communicative intentions there has been little work on automatically optimizing an agent s choices when there are multiple ways to realize a communicative intention. Our method is based on a combination of learning algorithms and empirical evaluation techniques. The learning component of our method is based on algorithms for reinforcement learning such as dynamic programming and Q-leaming. The empirical component uses the PARADISE evaluation framework Walker et al. 1997 to identify the important performance factors and to provide the performance function needed by the learning algorithm. We illustrate our method with a dialogue agent named ELVIS EmaiL Voice Interactive System that supports access to email over the phone. We show how ELVIS can learn to choose among alternate strategies for agent initiative for reading messages and for summarizing email folders. 1 Introduction This paper describes a novel method by which a dialogue agent can learn to choose an optimal dialogue strategy. The main problem for dialogue agents is deciding what information to communicate to a hearer and how and when to communicate it. For example consider one of the strategy choices faced by a spoken dialogue agent that accesses email by phone. When multiple messages match the user s query . Read my messages from Kim an email agent must choose among multiple response strategies. The agent might choose the Read-First .

• Báo cáo khoa học
• Learning Optimal Dialogue Strategies
• A Case Study
• Spoken Dialogue Agent for Email
• 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