Papers with Convolutional neural networks
Adaptive Convolution for Multi-Relational Learning (N19-1)
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| Challenge: | Existing convolutional neural networks fail to model full interactions between entities and relations, which limits the performance of link prediction. |
| Approach: | They propose a convolutional network that maximizes entity-relation interactions in a convergent fashion. |
| Outcome: | The proposed convolutional network performs better than baseline models on multiple datasets. |
Learning Context-Sensitive Convolutional Filters for Text Processing (D18-1)
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| Challenge: | Convolutional neural networks (CNNs) are a popular building block for natural language processing . despite their success, most existing CNN models share the same learned set of filters for all input sentences. |
| Approach: | They propose to use a meta network to learn context-sensitive convolutional filters for text processing by using a bidirectional filter generation mechanism. |
| Outcome: | The proposed framework outperforms standard and attention-based CNN models on four different tasks. |
Controlling Length in Abstractive Summarization Using a Convolutional Neural Network (D18-1)
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| Challenge: | Convolutional neural networks (CNNs) can't generate summaries of desired lengths due to space or length constraints. |
| Approach: | They propose an approach to constrain the summary length by extending a convolutional sequence to sequence model. |
| Outcome: | The proposed model outperforms baseline models in terms of ROUGE score, length variations and semantic similarity. |