Papers with sequence to sequence learning
Surprisingly Easy Hard-Attention for Sequence to Sequence Learning (D18-1)
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| Challenge: | Existing attention mechanisms are hard and hard, but they are more accurate when trained. |
| Approach: | They propose to use a beam approximation of the joint distribution between attention and output to train sequence to sequence learning. |
| Outcome: | The proposed method is compared to existing attention mechanisms on five translation tasks and shows consistent gains on the same tasks. |
Double Path Networks for Sequence to Sequence Learning (C18-1)
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| Challenge: | Existing approaches for Sequence to Sequence learning have been developed . convolutional neural networks and self-attention networks are the most popular . |
| Approach: | They propose to integrate convolutional and self-attention layers into a double path network for sequence to sequence learning. |
| Outcome: | The proposed method significantly improves performance over state-of-the-art systems. |
Parallel Iterative Edit Models for Local Sequence Transduction (D19-1)
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| Challenge: | Recent approaches to local sequence transduction are based on the popular encoder-decoder model for sequence to sequence learning. |
| Approach: | They propose a parallel iterative edit model for the problem of local sequence transduction arising in tasks like Grammatical error correction (GEC). |
| Outcome: | The proposed model is faster and more accurate than the current encoder-decoder model for local sequence transduction tasks like translation and paraphrasing. |