Papers with double-attentive decoder
Tailoring Neural Architectures for Translating from Morphologically Rich Languages (C18-1)
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| Challenge: | A morphologically complex word is a hierarchical constituent with meaning-preserving subunits, so word-based models which rely on surface forms might not be powerful enough to translate such structures. |
| Approach: | They propose a neural architecture which is designed to deal with morphological complexities on the source side and redesign the decoder accordingly to benefit from such information. |
| Outcome: | The proposed model outperforms existing subword- and character-based architectures and showed significant improvements on translating from German, Russian, and Turkish into English. |