博碩士論文 110552014 完整後設資料紀錄

DC 欄位 語言
DC.contributor資訊工程學系在職專班zh_TW
DC.creator周建豪zh_TW
DC.creatorChien-Hao Chouen_US
dc.date.accessioned2025-1-16T07:39:07Z
dc.date.available2025-1-16T07:39:07Z
dc.date.issued2025
dc.identifier.urihttp://ir.lib.ncu.edu.tw:444/thesis/view_etd.asp?URN=110552014
dc.contributor.department資訊工程學系在職專班zh_TW
DC.description國立中央大學zh_TW
DC.descriptionNational Central Universityen_US
dc.description.abstract隨著智慧家庭市場經濟規模的擴大,許多專屬的封閉生態系統逐漸形成, 例如 Apple 的 HomeKit、Google 的 Nest 和 Amazon 的 Alexa 等。這種封閉 的生態系統不僅限制了用戶的選擇,還迫使用戶學習不同系統的操作方式,難 以全面體驗智慧家庭應有的便利性。 本文旨在透過建構智慧家庭任務導向式對話系統,降低用戶的學習曲線 並突破封閉生態的問題。在建立任務導向式對話系統之前,需要先收集對話 語料庫。相較於傳統的人對人或人對機器語料收集方式,本文參考了 SchemaGuided Dialogue (SGD) 方法,並建構了 SmartHomeSGD 對話模擬器。透過 環境感知的機率方法決策用戶代理與助理代理的對話行為,使用戶代理人和助 理代理人能更有效地模擬人與系統的交互方式。 在對話行為設計方面,我們針對用戶代理和助理代理設計了專屬的對話 行為。此外,為了實現任務導向式對話系統與外部智慧家庭服務(傳統裝置、 多媒體播放器和暖通空調服務)的串接,我們加入了助理代理的 EXECUTE 對話行為,藉由此對話行為,發送 HTTP 請求,串接外部的智慧家庭服務。 在傳統機器對機器的語料生成中,機器生成的對話大綱往往需要大量人 力進行改寫,以提升語料的多樣性與自然性。為了解決此問題,本文設計了對 話改寫提示,以此引導大型語言模型執行對話改寫任務,有效降低人力成本。 最後,本文使用 mT5 (Multilingual Text-to-Text Transfer Transformer) 預 訓練模型作為基礎,並基於 SmartHomeSGD 語料庫進行微調,成功建構了中 文智慧家庭任務導向式對話系統。zh_TW
dc.description.abstractWith the expansion of the smart home market, many proprietary closed ecosystems have gradually emerged, such as Apple’s HomeKit, Google’s Nest, and Amazon’s Alexa. These closed ecosystems not only limit users’choices but also require them to learn different system operation methods, making it difficult to fully experience the convenience that smart homes should offer. This paper aims to address the learning curve and the issue of closed ecosystems by constructing a task-oriented dialogue system for smart homes. Before building the task-oriented dialogue system, a dialogue corpus must first be collected. Compared to traditional methods of collecting human-to-human or human-to-machine dialogues, this paper refers to the Schema-Guided Dialogue (SGD) approach and constructs the SmartHomeSGD dialogue simulator. Using a context-aware probabilistic method, the dialogue actions of the user agent and assistant agent are decided, allowing the agents to more effectively simulate the interactions between humans and systems. In the design of dialogue actions, we created specific actions for both the user agent and the assistant agent. Additionally, to integrate the task-oriented dialogue system with external smart home services (such as traditional devices, media players, and HVAC services), we added the EXECUTE dialogue action for the assistant agent. Through this action, HTTP requests are sent to connect to external smart home services. In traditional machine-to-machine dialogue corpus generation, the dialogue outlines generated by machines often require a significant amount of manual effort to revise in order to increase diversity and naturalness. To address this issue, this paper designs dialogue rewriting prompts to guide large language models in performing dialogue rewriting tasks, effectively reducing human labor costs. Finally, this paper uses the mT5 (Multilingual Text-to-Text Transfer Transformer) pre-trained model and fine-tunes it based on the SmartHomeSGD corpus to successfully construct a chinese task-oriented dialogue system for smart homesen_US
DC.subject智慧家庭zh_TW
DC.subject自然語言處理zh_TW
DC.subject任務導向式對話系統zh_TW
DC.subject語料庫建構zh_TW
DC.subject綱要引導式對話zh_TW
DC.subjectSmart Homeen_US
DC.subjectNatural Language Processingen_US
DC.subjectTask-Oriented Dialogue Systemen_US
DC.subjectCorpus Constructionen_US
DC.subjectSchema-Guided Dialogueen_US
DC.title為智慧家庭建構任務導向式對話系統zh_TW
dc.language.isozh-TWzh-TW
DC.titleBuilding Task-Oriented Dialogue Systems For Smart Homeen_US
DC.type博碩士論文zh_TW
DC.typethesisen_US
DC.publisherNational Central Universityen_US

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