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. |
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| Challenge: | Convolutional neural networks (CNNs) use recurrent neural networks as convolution filters to capture language compositionality and long-term dependencies. |
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| Challenge: | Recent work by Hewitt et al. (2020) provides an interpretation of the empirical success of recurrent neural networks (RNNs) as language models (LMs). |
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Lightweight Text Classifier using Sinusoidal Positional Encoding (2020.aacl-main)
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| Challenge: | Large and complex models require many parameters and time to solve various problems in natural language processing. |
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