홈 › Papers

Composite Task-Completion Dialogue Policy Learning via Hierarchical Deep Reinforcement Learning

2017-04-10 · EMNLP 2017 9 · Baolin Peng, Xiujun Li, Lihong Li, Jianfeng Gao, Asli Celikyilmaz, Sungjin Lee, Kam-Fai Wong

Building a dialogue agent to fulfill complex tasks, such as travel planning, is challenging because the agent has to learn to collectively complete multiple subtasks. For example, the agent needs to reserve a hotel and book a flight so that there leaves enough time for commute between arrival and hotel check-in. This paper addresses this challenge by formulating the task in the mathematical framework of options over Markov Decision Processes (MDPs), and proposing a hierarchical deep reinforcement learning approach to learning a dialogue manager that operates at different temporal scales. The dialogue manager consists of: (1) a top-level dialogue policy that selects among subtasks or options, (2) a low-level dialogue policy that selects primitive actions to complete the subtask given by the top-level policy, and (3) a global state tracker that helps ensure all cross-subtask constraints be satisfied. Experiments on a travel planning task with simulated and real users show that our approach leads to significant improvements over three baselines, two based on handcrafted rules and the other based on flat deep reinforcement learning.

📄 PDF Abstract BibTeX arXiv:1704.03084

Code (0)

등록된 구현이 없습니다.

Tasks

Deep Reinforcement Learningreinforcement-learning Reinforcement Learning Reinforcement Learning (RL)Task-Completion Dialogue Policy Learning

Autonomous Sub-domain Modeling for Dialogue Policy with Hierarchical Deep Reinforcement Learning

2018-10-01 · WS 2018 10 · Giovanni Yoko Kristianto, Huiwen Zhang, Bin Tong, Makoto Iwayama 외

Solving composites tasks, which consist of several inherent sub-tasks, remains a challenge in the research area of dialogue. Current studies have tackled this issue by manually decomposing the composite tasks into severa…

Deep Reinforcement Learning Hierarchical Reinforcement Learning reinforcement-learning Reinforcement Learning+1

Structured Hierarchical Dialogue Policy with Graph Neural Networks

2020-09-22 · Zhi Chen, Xiaoyuan Liu, Lu Chen, Kai Yu

Dialogue policy training for composite tasks, such as restaurant reservation in multiple places, is a practically important and challenging problem. Recently, hierarchical deep reinforcement learning (HDRL) methods have …

Deep Reinforcement Learning

HierTOD: A Task-Oriented Dialogue System Driven by Hierarchical Goals

2024-11-11 · Lingbo Mo, Shun Jiang, Akash Maharaj, Bernard Hishamunda 외

Task-Oriented Dialogue (TOD) systems assist users in completing tasks through natural language interactions, often relying on a single-layered workflow structure for slot-filling in public tasks, such as hotel bookings. …

Dialogue Management Management Natural Language Understanding Response Generation+2

Subgoal Discovery for Hierarchical Dialogue Policy Learning

2018-04-20 · EMNLP 2018 10 · Da Tang, Xiujun Li, Jianfeng Gao, Chong Wang 외

Developing agents to engage in complex goal-oriented dialogues is challenging partly because the main learning signals are very sparse in long conversations. In this paper, we propose a divide-and-conquer approach that d…

Hierarchical Reinforcement Learning Reinforcement Learning

Deep Dyna-Q: Integrating Planning for Task-Completion Dialogue Policy Learning

2018-01-18 · ACL 2018 7 · Baolin Peng, Xiujun Li, Jianfeng Gao, Jingjing Liu 외

Training a task-completion dialogue agent via reinforcement learning (RL) is costly because it requires many interactions with real users. One common alternative is to use a user simulator. However, a user simulator usua…

Reinforcement Learning Reinforcement Learning (RL)Task-Completion Dialogue Policy Learning