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Convolutional Neural Network Architectures for Matching Natural Language Sentences
TLDR
Convolutional neural network models for matching two sentences are proposed, by adapting the convolutional strategy in vision and speech and nicely represent the hierarchical structures of sentences with their layer-by-layer composition and pooling. Expand
Incorporating Copying Mechanism in Sequence-to-Sequence Learning
TLDR
This paper incorporates copying into neural network-based Seq2Seq learning and proposes a new model called CopyNet with encoder-decoder structure which can nicely integrate the regular way of word generation in the decoder with the new copying mechanism which can choose sub-sequences in the input sequence and put them at proper places in the output sequence. Expand
Neural Responding Machine for Short-Text Conversation
TLDR
Empirical study shows that NRM can generate grammatically correct and content-wise appropriate responses to over 75% of the input text, outperforming state-of-the-arts in the same setting, including retrieval-based and SMT-based models. Expand
Modeling Coverage for Neural Machine Translation
TLDR
This paper proposes coverage-based NMT, which maintains a coverage vector to keep track of the attention history and improves both translation quality and alignment quality over standard attention- based NMT. Expand
Multimodal Convolutional Neural Networks for Matching Image and Sentence
TLDR
The m-CNN provides an end-to-end framework with convolutional architectures to exploit image representation, word composition, and the matching relations between the two modalities to significantly outperform the state-of-the-art approaches for bidirectional image and sentence retrieval on the Flickr8K and Flickr30K datasets. Expand
Constrained spectral clustering through affinity propagation
TLDR
Experiments show the proposed method outperforms state-of-the-art constrained clustering methods in getting good clusterings with fewer constraints, and yields good image segmentation with user-specified pairwise constraints. Expand
A Dataset for Research on Short-Text Conversations
TLDR
This paper introduces a dataset of short-text conversation based on the real-world instances from Sina Weibo, which provides rich collection of instances for the research on finding natural and relevant short responses to a given short text, and useful for both training and testing of conversation models. Expand
Clustering with Multiple Graphs
TLDR
Experiments on SIAM journal data show that LMF can improve the clustering accuracy through fusing multiple sources of information with several models, and LMF yields superior or competitive results compared to other graph-based clustering methods. Expand
A Deep Architecture for Matching Short Texts
TLDR
This paper proposes a new deep architecture to more effectively model the complicated matching relations between two objects from heterogeneous domains and applies this model to matching tasks in natural language, e.g., finding sensible responses for a tweet, or relevant answers to a given question. Expand
An Information Retrieval Approach to Short Text Conversation
TLDR
This paper proposes formalizing short text conversation as a search problem at the first step, and employing state-of-the-art information retrieval techniques to carry out the task, investigating the significance as well as the limitation of the IR approach. Expand
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