tailieunhanh - Báo cáo khoa học: "Evaluating a Trainable Sentence Planner for a Spoken Dialogue System"

Techniques for automatically training modules of a natural language generator have recently been proposed, but a fundamental concern is whether the quality of utterances produced with trainable components can compete with hand-crafted template-based or rulebased approaches. In this paper We experimentally evaluate a trainable sentence planner for a spoken dialogue system by eliciting subjective human judgments. In order to perform an exhaustive comparison, we also evaluate a hand-crafted template-based generation component, two rule-based sentence planners, and two baseline sentence planners. We show that the trainable sentence planner performs better than the rule-based systems and the baselines, and as well. | Evaluating a Trainable Sentence Planner for a Spoken Dialogue System Owen Rambow AT T Labs - Research Florham Park NJ USA rambow@ Monica Rogati Carnegie Mellon University Pittsburgh PA USA mrogati @ Marilyn A. Walker AT T Labs - Research Florham Park NJ USA walker@ Abstract Techniques for automatically training modules of a natural language generator have recently been proposed but a fundamental concern is whether the quality of utterances produced with trainable components can compete with hand-crafted template-based or rulebased approaches. In this paper We experimentally evaluate a trainable sentence planner for a spoken dialogue system by eliciting subjective human judgments. In order to perform an exhaustive comparison we also evaluate a hand-crafted template-based generation component two rule-based sentence planners and two baseline sentence planners. We show that the trainable sentence planner performs better than the rule-based systems and the baselines and as well as the handcrafted system. 1 Introduction The past several years have seen a large increase in commercial dialog systems. These systems typically use system-initiative dialog strategies with system utterances highly scripted for style and register and recorded by voice talent. However several factors argue against the continued use of these simple techniques for producing the system side of the conversation. First text-to-speech has improved to the point of being a viable alternative to pre-recorded prompts. Second there is a perceived need for spoken dialog systems to be more flexible and support user initiative but this requires greater flexibility in utter ance generation. Finally systems to support complex planning are being developed which will require more sophisticated output. As we move away from systems with prerecorded prompts there are two possible approaches to producing system utterances. The first is template-based generation where .

• Báo cáo khoa học
• Evaluating a Trainable
• Sentence Planner
• a Spoken Dialogue System
• 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