TAILIEUCHUNG - Báo cáo khoa học: "Trainable Generation of Big-Five Personality Styles through Data-driven Parameter Estimation"

Previous work on statistical language generation has primarily focused on grammaticality and naturalness, scoring generation possibilities according to a language model or user feedback. More recent work has investigated data-driven techniques for controlling linguistic style without overgeneration, by reproducing variation dimensions extracted from corpora. Another line of work has produced handcrafted rule-based systems to control specific stylistic dimensions, such as politeness and personality. . | Trainable Generation of Big-Five Personality Styles through Data-driven Parameter Estimation Francois Mairesse Cambridge University Engineering Department Trumpington Street Cambridge CB2 1PZ United Kingdom farm2@ Marilyn Walker Department of Computer Science University of Sheffield Sheffield S1 4Dp United Kingdom lynwalker@ Abstract Previous work on statistical language generation has primarily focused on grammaticality and naturalness scoring generation possibilities according to a language model or user feedback. More recent work has investigated data-driven techniques for controlling linguistic style without overgeneration by reproducing variation dimensions extracted from corpora. Another line of work has produced handcrafted rule-based systems to control specific stylistic dimensions such as politeness and personality. This paper describes a novel approach that automatically learns to produce recognisable variation along a meaningful stylistic dimension personality without the computational cost incurred by overgeneration techniques. We present the first evaluation of a data-driven generation method that projects multiple personality traits simultaneously and on a continuous scale. We compare our performance to a rule-based generator in the same domain. 1 Introduction Over the last 20 years statistical language models SLMs have been used successfully in many tasks in natural language processing and the data available for modeling has steadily grown Lapata and Keller 2005 . Langkilde and Knight 1998 first applied SLMs to statistical natural language generation SNLG showing that high quality paraphrases can be generated from an underspecified representation of meaning by first applying a very underconstrained rule-based overgeneration phase whose outputs are then ranked by an SLM scoring phase. Since then research in SNLG has explored a range of models for both dialogue and text generation. One line of work has primarily focused on .

• Báo cáo khoa học
• Trainable Generation
• Big Five Personality Styles
• Data driven Parameter Estimation
• báo cáo khoa học
• báo cáo 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 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