Papers with dynamic routing policy
Towards Linear Time Neural Machine Translation with Capsule Networks (D19-1)
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| Challenge: | Neural Machine Translation (NMT) is an endto-end learning approach to machine translation. |
| Approach: | They propose a capsule network with dynamic routing for linear time Neural Machine Translation . they map the source sentence into a matrix with pre-determined size and apply a deep LSTM network to decode the target sequence from the source representation. |
| Outcome: | The proposed network achieves comparable results with the Transformer system on English-German and English-French tasks. |
Information Aggregation via Dynamic Routing for Sequence Encoding (C18-1)
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| Challenge: | Currently, little attention is paid to how to aggregate text sequences into fixed-size vectors. |
| Approach: | They propose an aggregation mechanism to obtain a fixed-size encoding with a dynamic routing policy. |
| Outcome: | The proposed method outperforms other aggregating methods on five text classification tasks. |