[PDF] Top 20 Learning to Control the Specificity in Neural Response Generation
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Learning to Control the Specificity in Neural Response Generation
... There have been a few efforts attempting to ad- dress this issue in literature. Li et al. (2016a) proposed to use the Maximum Mutual Informa- tion (MMI) as the objective to penalize general re- sponses. It could be ... See full document
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Neural Response Generation with Meta words
... of generation and dynamically monitors expression of each vari- able in the meta-word during the decoding pro- ...the learning of the memory network under the ground ... See full document
11
A Neural Network Approach to Context Sensitive Generation of Conversational Responses
... from response generation (Stent and Bangalore, 2014), while our holistic approach can be considered a first attempt to accomplish both tasks ...chine learning for response generation ... See full document
10
Augmenting Neural Response Generation with Context-Aware Topical Attention
... in learning the backbone of the conversa- tion but lacks any aptitude for producing context- sensitive and diverse ...multi-turn response generation system intended to produce context-aware and ... See full document
14
Conditional Generation and Snapshot Learning in Neural Dialogue Systems
... conditional generation architectures and a novel method called snapshot learning to improve response generation in a neural dialogue system ...snapshot learning pro- vided gains ... See full document
10
Retrieval Enhanced Adversarial Training for Neural Response Generation
... Generation-based methods can be cast as a se- quence to sequence (Seq2Seq) process (Shang et al., 2015; Vinyals and Le, 2015; Sordoni et al., 2015) but suffers from generating generic re- sponses. One way to ... See full document
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Neural Response Generation via GAN with an Approximate Embedding Layer
... To the best of our knowledge, Reinforcement Learning (RL) is first introduced to address the above problem (Li et al., 2017; Yu et al., 2017), where the score predicted by a discriminator was used as the ... See full document
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Learning to Abstract for Memory augmented Conversational Response Generation
... the response generation. Our model clusters query-response samples, ex- tracts characteristics of each cluster, and learns to utilize these characteristics for response ... See full document
10
Group Linguistic Bias Aware Neural Response Generation
... Table 1: Responses from two user groups (A & B) categorized by user gender. The examples are selected from the real Chinese Social-Network- Service (SNS) dataset and translated into English. In real world data, it is ... See full document
10
Jointly Optimizing Diversity and Relevance in Neural Response Generation
... target response vector (ar- rowed lines in Figure ...can control relevance and diversity by respectively adjusting distance and direction from a predicted response vector, without sacrificing each ... See full document
10
Multi turn Dialogue Response Generation in an Adversarial Learning Framework
... Serban et al. (2016) and Xing et al. (2017) proposed the Hierarchical Recurrent Encoder- Decoder (HRED) network to capture long tempo- ral dependencies in multi-turn conversations to ad- dress the limited contextual ... See full document
12
Steering Output Style and Topic in Neural Response Generation
... Having trained the grid on some corpus (in our case a sample of the base model’s corpus), the mapping of either a source S and/or target T sen- tence can be obtained by treating the sentences as bags of words. By ... See full document
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Neural Response Generation for Customer Service based on Personality Traits
... initial learning rate of 0.1, and halved the learning rate every epoch, starting from epoch ...correct response was maximized using stochastic gradient descent with a batch size set to 64, and ... See full document
5
Adversarial Learning for Neural Dialogue Generation
... the generative model can only be indirectly exposed to the gold-standard target sequences through the reward passed back from the discriminator, and this reward is used to promote or discourage its (the generator’s) own ... See full document
13
Hierarchy Response Learning for Neural Conversation Generation
... safe response patterns in the open domain conver- ...reinforcement learning (Zhang et ...the response generation model cannot express the in- tention and emotion ... See full document
10
Neural Keyphrase Generation via Reinforcement Learning with Adaptive Rewards
... To predict both present and absent keyphrases for a document, Meng et al. (2017) proposed a generative model, CopyRNN, which is composed of an attentional encoder-decoder model (Bah- danau et al., 2014) and a copy ... See full document
12
MULTI-INTERSECTION TRAFFIC CONGESTION CONTROL METHOD BASED ON REINFORCEMENT LEARNING.
... optimal control action, namely the optimal regional shunt ...upper control is the messages between nodes , when the message tend to convergent,we get the optimal control action, namely the optimal ... See full document
9
Learning to Control the Fine grained Sentiment for Story Ending Generation
... The automatic and human evaluation results of four generation models are shown in Table 2 and Table 3 respectively. We have the following obser- vations: (1) Three models based on our proposed framework do not ... See full document
7
On The Design of Artificial Intelligence Based Load Frequency Controller for A Two Area Power System With Super Conducting Magnetic Energy Storage Device
... Frequency Control (LFC),or automatic generation control,is a very important issue in power system operation and control for supplying sufficient and reliable electric ...automatic ... See full document
9
Role of Immunoproteasome Catalytic Subunits in the Immune Response to Hepatitis B Virus
... CTL response to viral antigen in the absence of LMP2 or LMP7 ...immune response to LCMV in infected LMP2- and LMP7-deficient mice ...CTL response is unusually sensitive to immunoproteasome ...T-cell ... See full document
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