[PDF] Top 20 Deep Reinforcement Learning for Dialogue Generation
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Deep Reinforcement Learning for Dialogue Generation
... of reinforcement learning, which have been widely ap- plied in MDP and POMDP dialogue systems (see Re- lated Work section for ...ral reinforcement learning (RL) generation ... See full document
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Deep Reinforcement Learning for Interactive Narrative Planning.
... machine learning problems utilizing sequence data (Dietterich, 2002), deep learning methods offer an especially effective set of models in solving the simulated player modeling problem with sequence ... See full document
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Curriculum Learning Based on Reward Sparseness for Deep Reinforcement Learning of Task Completion Dialogue Management
... Progressive Neural Networks Originally, the notion of progressive neural networks is proposed in the research to transfer learning across multi- ple tasks and foreknowledge task similarity (Rusu et al., 2016a). ... See full document
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Using Reinforcement Learning to Build a Better Model of Dialogue State
... as Dialogue Acts (Forbes-Riley et ...the dialogue are called Shallow, answers that involve a novel concept are called Novel, “I don’t know” type answers are called Assertions (As), and Deep answers ... See full document
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Combining Hierarchical Reinforcement Learning and Bayesian Networks for Natural Language Generation in Situated Dialogue
... Hierarchical Reinforcement Learning (HRL) with Bayesian networks to achieve ...this. Reinforcement learning (RL) is an attractive framework for opti- mising NLG systems, where situations are ... See full document
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Autonomous Sub domain Modeling for Dialogue Policy with Hierarchical Deep Reinforcement Learning
... composite dialogue (Peng et ...ite dialogue of making a hotel reservation involves several sub-tasks, such as looking for a hotel that meets the user’s constraints, booking the room, and paying for the ... See full document
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Deep Learning for Dialogue Systems
... the dialogue manager to be optimized to plan and act under the uncertainty created by noisy speech recogni- tion and semantic decoding (Williams and Young, 2007; Young et ...episodic reinforcement ... See full document
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Deep Learning for Dialogue Systems
... Language Generation The RNN-based models have been applied to language generation for both chit-chat and task-orientated dialogue systems (Vinyals and Le, 2015; Wen et ...the dialogue act ... See full document
7
Feudal Reinforcement Learning for Dialogue Management in Large Domains
... Reinforcement learning (RL) is a promising approach to solve dialogue policy optimisa- ...the dialogue state space, tak- ing the decisions at each step using different parts of the abstracted ... See full document
6
Sentence Simplification with Deep Reinforcement Learning
... a reinforcement learning framework (Williams, 1992): it explores the space of possible simplifications while learn- ing to maximize an expected reward function that encourages outputs which meet ... See full document
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Composite Task Completion Dialogue Policy Learning via Hierarchical Deep Reinforcement Learning
... All the work above focuses on single-domain problems. Extensions to composite-domain dia- logue problems are non-trivial due to several rea- sons: the state and action spaces are much larger, the trajectories are much ... See full document
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Paraphrase Generation with Deep Reinforcement Learning
... Automatic evaluation Table 2 shows the per- formances of the models on Quora datasets. In both settings, we find that the proposed RbM- SL and RbM-IRL models outperform the baseline models in terms of all the evaluation ... See full document
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Dialogue Generation: From Imitation Learning to Inverse Reinforcement Learning
... open-domain dialogue system is to gener- ate sensible dialogue responses given a dialogue context (Ritter, Cherry, and Dolan 2010; Shang, Lu, and Li 2015; Li et ...a dialogue generation ... See full document
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Evaluating Persuasion Strategies and Deep Reinforcement Learning methods for Negotiation Dialogue agents
... With the aim of studying strategic conversations, a corpus of online trading chats between humans playing “Settlers of Catan” was collected (Afan- tenos et al., 2012). The JSettlers implementa- tion of the game was ... See full document
5
A Comparative Study of Reinforcement Learning Techniques on Dialogue Management
... SARSA(λ) performed almost equally to IAC at the experiment with deterministic transitions but did not react well to the change in q. As we can see in Table 6, SARSA(λ) generally con- verges at around episode 29 for a ... See full document
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Policy Networks with Two Stage Training for Dialogue Systems
... supervised learning (Silver et ...Supervised learning of the policy was one of the first techniques used to solve this prob- lem (Pomerleau, 1989; Amit and Mataric, ...imitation learning requires ... See full document
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Reinforcement Learning of Two Issue Negotiation Dialogue Policies
... reservations (Henderson et al., 2008), sightsee- ing recommendations (Misu et al., 2010), appoint- ment scheduling (Georgila et al., 2010), techni- cal support (Janarthanam and Lemon, 2010), etc., largely ignoring other ... See full document
5
Reinforcement Learning of Multi Issue Negotiation Dialogue Policies
... We use reinforcement learning (RL) to learn a multi-issue negotiation dialogue policy. For training and evaluation, we build a hand-crafted agenda-based pol- icy, which serves as the negotiation ... See full document
5
Exploring Deep Reinforcement Learning with Multi Q Learning
... When the standard deviation of the reward function was increased, the deviation in the value estimate increased for each algorithm. When using an 𝜀𝜀-greedy behavior pol- icy, Q-learning and Double ... See full document
16
Learning Optimal Dialogue Management Rules by Using Reinforcement Learning and Inductive Logic Programming
... [reinforcement learning] to the problem of op- timizing dialogue strategy selection ...The dialogue is about activities in New ...its dialogue strategy by allowing or not allowing users ... See full document
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