Papers by Yuhuang Hu
Character-Level Translation with Self-attention (2020.acl-main)
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| Challenge: | Existing models for character-level neural machine translation operate on word-level, which makes them memory inefficient because of large vocabulary sizes. |
| Approach: | They propose a transformer-based model and a novel variant that uses convolutions to combine information from nearby characters to facilitate character interactions. |
| Outcome: | The proposed model outperforms the standard transformer model and learns more robust character alignments on bilingual and multilingual translation datasets. |