[PDF] Top 20 Neural Responding Machine for Short Text Conversation
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Neural Responding Machine for Short Text Conversation
... and f(·) is a non-linear activation function and the transformation L is often assigned as pa- rameters of f (·). Here f (·) can be a logistic function, the sophisticated long short-term mem- ory (LSTM) unit ... See full document
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Towards Implicit Content Introducing for Generative Short Text Conversation Systems
... standard neural network with specially designed gate to control the cue word, but the results vary ...the neural cell has the similar result as FGRU method, which concatenates cue word with hidden ... See full document
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Conversing by Reading: Contentful Neural Conversation with On demand Machine Reading
... Although neural conversation models are ef- fective in learning how to produce fluent re- sponses, their primary challenge lies in know- ing what to say to make the conversation con- tentful and ... See full document
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Sequential Short Text Classification with Recurrent and Convolutional Neural Networks
... for short-text classification, such as using Support Vector Machines (SVMs) with rule-based features (Silva et ...convolutional neural networks (CNNs) (Kim, 2014; Blunsom et ...recursive ... See full document
6
Unified Framework For Deep Learning Based Text Classification
... artificial neural networks, which are inspired by biological brain model made of ...convolutional neural network (CNN), deep belief networks, recurrent neural networks (RNN), long short term ... See full document
5
Neural Machine Translation of Text from Non Native Speakers
... Neural Machine Translation (NMT) is undeniably a success story: public benchmarks (Bojar et ...by neural systems, and neu- ral approaches are the de facto option for industrial systems (Wu et ... See full document
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Deep Learning Approach for Text Generation Using RNN Encoder-Decoder for Q&A
... of neural network based on encoder and decoder for machine translation and new type of cell called Gated Recurrent Unit ...The neural network proposed in article encodes source language sentence in ... See full document
6
A Persona Based Neural Conversation Model
... statistical machine transla- tion (SMT) ...Long Short-Term Memory (LSTM) (Hochreiter and Schmidhuber, 1997) to learn from unaligned data in order to reduce the heuristic space of sentence planning and ... See full document
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Sequence to Backward and Forward Sequences: A Content Introducing Approach to Generative Short Text Conversation
... of neural networks brings new opportunities to open-domain conver- sation (Vinyals and Le, 2015; Shang et ...benefiting machine translation (Sutskever et ...methods, neural network-based ... See full document
10
Semantic Hashing to Understanding short text using Neural Networks
... Recurrent Neural Network (DRNN) model associated with stacked denoising auto encoder (AE) to capture the semantics of the short ...Recurrent Neural Networks (RNNs) have shown great results in ... See full document
6
Generating Multiple Diverse Responses for Short-Text Conversation
... on short-text conversation ...two short-text conversation tasks validate that the multiple responses generated by our model obtain higher quality and larger diversity compared ... See full document
8
A Discrete CVAE for Response Generation on Short Text Conversation
... lize a set of latent embeddings to model diverse responding mechanisms. Xing et al. (2017) intro- duce pre-defined topics from an external corpus to augment the information used in response gener- ation. Gao et ... See full document
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Fine Grained Sentence Functions for Short Text Conversation
... of conversation mod- ...new Short-Text Conversation dataset with manually annotated SEntence FUNctions ...of short-text conver- sations; (ii) estimate a proper sentence func- ... See full document
10
Cluster Gated Convolutional Neural Network for Short Text Classification
... The second strategy is to explore latent topics or clustering features for classification. For example, Chen et al. (2011) derived multi-granularity topics through latent Dirichlet allocation (LDA) as features for ... See full document
10
Semantic Clustering and Convolutional Neural Network for Short Text Categorization
... of short text using latent semantics, where the words are mapped to distributional representa- tions by Latent Dirichlet Allocation (LDA) (Blei et ...the short and sparse text by appending ... See full document
6
Deep Learning For Anticipation Of Cardiovascular Disease: A Practical Approach
... IndRNN can be easily regulated and the network can learn lengthy-term dependency basis to deter the gradient from erupting and vanishing problems. To create a profound network than the extant RNNs, multiple IndRNNs can ... See full document
6
Learning Word Representations with Cross Sentence Dependency for End to End Co reference Resolution
... Long short-term memory (LSTM) networks (Hochreiter and Schmidhuber, 1997) are widely used for sentence modeling. A single-layer LSTM network was applied in the previous state-of-the- art co-reference model (Lee et ... See full document
5
Evaluating Human-Machine Conversation for Appropriateness
... the conversation is covered by user sat- ...poor text-to-speech component performance can have a disproportional effect on user ...a conversation based on the progression of the dialogue so ... See full document
8
Cascade neural fuzzy model of analysis of short electronic unstructured text documents using expert information
... A short EUTD is a text document written in natural language and containing information in linguistic or digital ...statistical text analysis, but permits the use of expert information obtained as a ... See full document
6
Sequential Matching Network: A New Architecture for Multi turn Response Selection in Retrieval Based Chatbots
... (1) Logical consistency. SMN models the con- text and response on the semantic level, but pays little attention to logical consistency. This leads to several DSATs in the Douban Corpus. For exam- ple, given a ... See full document
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