TAILIEUCHUNG - Báo cáo khoa học: "Spoken Dialogue Management Using Probabilistic Reasoning"

Spoken dialogue managers have benefited from using stochastic planners such as Markov Decision Processes (MDPs). However, so far, MDPs do not handle well noisy and ambiguous speech utterances. We use a Partially Observable Markov Decision Process (POMDP)-style approach to generate dialogue strategies by inverting the notion of dialogue state; the state represents the user’s intentions, rather than the system state. | Spoken Dialogue Management Using Probabilistic Reasoning Nicholas Roy and Joelle Pineau and Sebastian Thrun Robotics Institute Carnegie Mellon University ________ . Pittsburgh PA 15213 I . eba. @c. . Abstract Spoken dialogue managers have benefited from using stochastic planners such as Markov Decision Processes MDPs . However so far MDPs do not handle well noisy and ambiguous speech utterances. We use a Partially Observable Markov Decision Process POMDP -style approach to generate dialogue strategies by inverting the notion of dialogue state the state represents the user s intentions rather than the system state. We demonstrate that under the same noisy conditions a POMDP dialogue manager makes fewer mistakes than an MDP dialogue manager. Furthermore as the quality of speech recognition degrades the POMDP dialogue manager automatically adjusts the policy. 1 Introduction The development of automatic speech recognition has made possible more natural human-computer interaction. Speech recognition and speech understanding however are not yet at the point where a computer can reliably extract the intended meaning from every human utterance. Human speech can be both noisy and ambiguous and many real-world systems must also be speaker-independent. Regardless of these difficulties any system that manages human-machine dialogues must be able to perform reliably even with noisy and stochastic speech input. Recent research in dialogue management has shown that Markov Decision Processes MDPs can be useful for generating effective dialogue strategies Young 1990 Levin et al. 1998 the system is modelled as a set of states that represent the dialogue as a whole and a set of actions corresponding to speech productions from the system. The goal is to maximise the reward obtained for fulfilling a user s request. However the correct way to represent the state of the dialogue is still an open problem Singh et al. 1999 . A common solution .

• Báo cáo khoa học
• Spoken Dialogue Management
• Probabilistic Reasoning
• Nicholas Roy and Joelle Pineau
• báo cáo khoa học
• mô hình ngôn ngữ
• xử lý ngôn ngữ tự nhiên
• Combining POMDPs trained
• User Simulations and Rule based Dialogue Management
• Spoken Dialogue System
• báo cáo ngôn ngữ
• An Architecture for Dialogue Management
• Context Tracking
• Pragmatic Adaptation in Spoken Dialogue Systems
• Context Management
• Topics for Spoken Dialogue Systems
• Kristiina Jokinen
• Multimodal Dialogue
• Domain Reasoning
• Dialogue Management
• Conclusion
• Wearable Computing
• Conversational Systems
• Advanced In Vehicle
• Spoken Dialog Corpus
• Dialogue System
• Robust Dialog Management
• Correcting Code
• Robust Speech Recognition
• 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