[PDF] Top 20 Hierarchy Response Learning for Neural Conversation Generation
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Hierarchy Response Learning for Neural Conversation Generation
... The neural encoder-decoder models have shown great promise in neural conversation ...sponse generation (HRG) framework is pro- posed to capture the conversation intention in a natural ... See full document
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A Neural Network Approach for Knowledge Driven Response Generation
... on neural networks approaches shows their great potential at tackling a wide variety of Natural Language Processing (NLP) tasks (Bengio et ...automatic response generation domain are based on ... See full document
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Group Linguistic Bias Aware Neural Response Generation
... the conversation corpora generated by human groups in the Social Network Services (SNS), into the encoder- decoder based response ...designed neural com- ponent to dynamically control the impact of ... See full document
10
Jointly Optimizing Diversity and Relevance in Neural Response Generation
... Reddit: As the Switchboard dataset is relatively small and multiple references are synthetically constructed, we have developed another multi- reference dataset by extracting posts and com- ments on Reddit.com during ... See full document
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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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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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Towards Coherent and Engaging Spoken Dialog Response Generation Using Automatic Conversation Evaluators
... Sequence-to-sequence (seq2seq) models (Sutskever et al., 2014) and their extensions (Lu- ong et al., 2015; Sordoni et al., 2015; Li et al., 2015), which are used for neural machine translation (MT), have been ... See full document
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Neural Conversation Model Controllable by Given Dialogue Act Based on Adversarial Learning and Label aware Objective
... We used the policy gradient in this research to update the parameters of the generator, which is based on expected reward calculation by MCTS. However, MCTS requires enormous calculation costs because it requires ... See full document
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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
Learning to Ask: Neural Question Generation for Reading Comprehension
... Question generation (QG) aims to create natu- ral questions from a given a sentence or para- ...question generation is in the area of education — to generate ques- tions for reading comprehension materials ... See full document
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Augmenting Neural Response Generation with Context-Aware Topical Attention
... swer and the human-generated answer, which can be inconsistent with what humans deem a plau- sible and interesting response. (Liu et al., 2016) have showed that these metrics correlate very weakly with human ... See full document
14
Learning to Abstract for Memory augmented Conversational Response Generation
... Although current retrieval-augmented genera- tive models have achieved promising results, they still have following weaknesses: Firstly, they are limited by the quality of the retrieved results. Retrieval results are ... See full document
10
Neural Response Generation for Customer Service based on Personality Traits
... 30 conversation pairs, so we would have sufficient data for each agent to extract their personality traits (see ...87.5K conversation pairs in total including 633 dif- ferent agents (138 ±160 pairs per ... See full document
5
Neural Response Generation via GAN with an Approximate Embedding Layer
... for response generation simulta- neously with a discriminative classifier that measures the differences between human-produced responses and machine- generated ...traditional neural response ... See full document
10
A Neural Network Approach to Context Sensitive Generation of Conversational Responses
... ford flexibility to model the transitions between con- secutive utterances and to capture long-span depen- dencies in a domain where traditional word and phrase alignment is difficult (Ritter et al., 2011). To this end, ... See full document
10
A Persona Based Neural Conversation Model
... inconsistent response de- cisions, generating different answers in the sec- ond example in response to questions asking about age or ...persona-specific generation model and a general conversational ... See full document
10
Exemplar Encoder Decoder for Neural Conversation Generation
... The EED model scores better than generative models on almost all of the metrics, indicating that we generate more informative responses than other state-of-the-art generative based approaches for Ubuntu corpus. The ... See full document
10
Adversarial Learning for Neural Dialogue Generation
... One caveat with the adversarial evaluation methods is that they are model-dependent. We approximate the human evaluator in the Turing test with an au- tomatic evaluator and assume that the evaluator is perfect: low ... See full document
13
Multi Task Learning for Speaker Role Adaptation in Neural Conversation Models
... rent response given the previous ...multi-task learning strat- egy that predicts the response given the same se- quence, but with the target parameters tied with the general conversational ... See full document
10
Deep Learning Approach for Text Generation Using RNN Encoder-Decoder for Q&A
... Deep Learning plays important role in building such system which show ...Deep Neural Networks (DNNs) are powerful models which have already achieved good performance for different machine learning ... See full document
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