Papers with convolution filters
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. |
Convolutional Neural Networks with Recurrent Neural Filters (D18-1)
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| Challenge: | Convolutional neural networks (CNNs) use recurrent neural networks as convolution filters to capture language compositionality and long-term dependencies. |
| Approach: | They propose to use recurrent neural networks (RNNs) as convolution filters to capture language compositionality and long-term dependencies. |
| Outcome: | The proposed convolutional neural networks achieve state-of-the-art on two sentences and the Stanford Sentiment Treebank. |
A Deep Relevance Model for Zero-Shot Document Filtering (P18-1)
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| Challenge: | Existing methods for document classification do not consider document filtering . existing methods do not include document filter. |
| Approach: | They propose a deep relevance model for zero-shot document filtering called DAZER . they use word embeddings to extract the relevance signals from word embeds . |
| Outcome: | The proposed model outperforms existing models on two document collections . it estimates the relevance between a document and a category by using seed words . |
Sequential Learning of Convolutional Features for Effective Text Classification (D19-1)
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| Challenge: | Existing models for text classification have largely ignored convolution filters and max pooling . text classification is one of the major applications of natural language processing . |
| Approach: | They propose a convolutional attentive recurrent network model which uses convolution filters and max pooling to improve text classification. |
| Outcome: | The proposed model outperforms existing convolutional models on text classification tasks. |