Papers with ITG RNN re-ordering model
Fast and Accurate Reordering with ITG Transition RNN (C18-1)
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| Challenge: | Attention-based sequence-to-sequence neural networks learn to jointly align and translate. |
| Approach: | They propose to use a reordering RNN that shares the input encoder with the decoder to decouple re-ordering from translation. |
| Outcome: | The proposed model can achieve superior reordering accuracy without feature engineering and is 2.5x faster in decoding. |