Refining Source Representations with Relation Networks for Neural Machine Translation (C18-1)
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| Challenge: | Existing neural machine translation frameworks that forget distant information and disregard relationship between source and target words are not effective. |
| Approach: | They propose to use relation networks to learn better representations of the source . they propose to associate source words with each other to help retain their relationships . |
| Outcome: | Experiments show that the proposed approach outperforms the encoder-decoder framework on several datasets. |
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