[PDF] Top 20 Hierarchical Reinforcement Learning for Adaptive Text Generation
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Hierarchical Reinforcement Learning for Adaptive Text Generation
... surface generation, all cho- sen actions are transformed into an SPL (Kasper, ...For generation of more than one instruction, aggregation has to take ...a text and inserting them into a larger SPL ... See full document
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Interactive Semantic Parsing for If-Then Recipes via Hierarchical Reinforcement Learning
... Semantic parsing aims to map natural language to formal domain-specific meaning representations, such as knowl- edge base or database queries (Berant et al. 2013; Dong and Lapata 2016; Zhong, Xiong, and Socher 2017; Gao, ... See full document
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Diversity-Driven Extensible Hierarchical Reinforcement Learning
... and MLSH are plotted in Figure 6 (upper part), which al- ways drop when the goal is changed, because the top-level hierarchies of both methods are re-initialized. Obviously, the increase speeds of the episode extrinsic ... See full document
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Adaptive Referring Expression Generation in Spoken Dialogue Systems: Evaluation with Real Users
... to learning user-adaptive referring expres- sion generation (REG) policies for spoken dialogue ...a reinforcement learning (RL) framework in which the system learns REG policies which ... See full document
8
ARAML: A Stable Adversarial Training Framework for Text Generation
... all generation steps to deal with long text generation ...missing text conditioned on the surrounding context, which is expected to mitigate the prob- lem of mode ...Inverse ... See full document
11
An Adaptive Hierarchical Clustering Algorithm for Segmenting Sentence level Text
... Multi-document summarization aims to generate a concise summary which has salient information given by a multitude of source documents. With this field, sentence ranking has hitherto been the subject of most concern ... See full document
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Accelerated Reinforcement Learning for Sentence Generation by Vocabulary Prediction
... Mini-batch splitting It should be noted that our small softmax method can be run even on the single GTX 1080 GPU for the larger translation datasets, whereas the full softmax method runs out of the GPU memory. A typical ... See full document
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Data to text Generation with Entity Modeling
... data-to-text generation have shown great promise thanks to the use of large-scale datasets and the application of neural network architectures which are trained ...tation learning to select content ... See full document
13
Multiple Action Sequence Learning and Automatic Generation for a Humanoid Robot Using RNNPB and Reinforcement Learning
... a reinforcement learning algorithm: Q-learning (QL) is adopt to determine which PB value is adaptive for the ...BPTT; Reinforcement Learning; Multiple Action Sequences ... See full document
6
Hierarchical Encoder with Auxiliary Supervision for Neural Table-to-Text Generation: Learning Better Representation for Tables
... encoder-decoder learning, namely auxiliary sequence labeling task, text auto- encoder and multi-labeling classification, as the auxiliary su- pervisions for the table ... See full document
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An Empirical Comparison on Imitation Learning and Reinforcement Learning for Paraphrase Generation
... Paraphrase generation has the potential of being used in many other NLP research topics, such as machine translation (Madnani et ...the learning algorithms are developed by uncovering the connection between ... See full document
7
Hierarchical Reinforcement Learning for Course Recommendation in MOOCs
... ment learning algorithm to solve many kinds of problems, such as relation classification (Feng et ...2018), text clas- sification (Zhang, Huang, and Zhao 2018), information ex- traction (Narasimhan, Yala, ... See full document
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Combining Hierarchical Reinforcement Learning and Bayesian Networks for Natural Language Generation in Situated Dialogue
... Figure 2 shows a (hand-crafted) hierarchy of learn- ing agents for navigating and acting in a situated en- vironment. Each agent represents an individual gen- eration task. The models shown in the bottom of the figure ... See full document
11
Hierarchical Reinforcement Learning and Hidden Markov Models for Task Oriented Natural Language Generation
... from data and in which the HMM represents the generation space of a surface realiser. We also proposed to jointly optimise surface realisation and content selection to balance the tradeoffs of (a) frequency in ... See full document
6
Neural Keyphrase Generation via Reinforcement Learning with Adaptive Rewards
... We conduct extensive experiments to evaluate the performance of our RL approach. Experi- ment results on five real-world datasets show that our RL approach consistently improves the per- formance of the state-of-the-art ... See full document
12
Composite Task Completion Dialogue Policy Learning via Hierarchical Deep Reinforcement Learning
... using reinforcement learning (RL); see Su et ...icy learning for composite tasks requires explo- ration in a much larger state-action space, and it often takes many more conversation turns between ... See full document
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Adaptive Policy-based Object Tracking using Reinforcement Learning.
... Supervised learning (SL), which means the data fed into the model is labeled by a human, can be the solution to prediction types of tasks. In other words, the model knows the correct definition of data during ... See full document
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Autonomous Sub domain Modeling for Dialogue Policy with Hierarchical Deep Reinforcement Learning
... task. Hierarchical reinforcement learning (HRL) (Dietterich, 2000; Parr and Rus- sell, 1997) is a technique to model complex di- alogues (Cuay´ahuitl, ...policy learning in a com- posite task, ... See full document
8
Framework of Automatic Text Summarization Using Reinforcement Learning
... Since our model of the state value function was simply linear and our parameter estimation was im- plemented by TD (λ), which is a simple method in RL, it seems simply employing more efficient or state-of-the-art ... See full document
10
Maximizing Throughput using Adaptive Routing Based on Reinforcement Learning
... The key purpose of middleware for sensor networks is to support the development, execution, deployment, and maintenance of sensing-based applications. This includes mechanisms for formulating complex sophisticated ... See full document
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