| 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. |
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| Challenge: | Currently, most neural machine translation models rely on pairs of parallel sentences, assuming syntactic information is automatically learned by an attention mechanism. |
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Rethinking Document-level Neural Machine Translation (2022.findings-acl)
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| Challenge: | Neural machine translation models are weak enough for document-level translation . current models only translate sentences individually, resulting in poor document coherence . |
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Understanding Pure Character-Based Neural Machine Translation: The Case of Translating Finnish into English (2020.coling-main)
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| Challenge: | Recent work shows that deeper character-based neural machine translation models outperform subword-based models. |
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Do Multilingual Neural Machine Translation Models Contain Language Pair Specific Attention Heads? (2021.findings-acl)
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| Challenge: | Neural models for morphological inflection have recently attained very high results, but their interpretation remains challenging. |
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Syntax-guided Localized Self-attention by Constituency Syntactic Distance (2022.findings-emnlp)
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Shengyuan Hou, Jushi Kai, Haotian Xue, Bingyu Zhu, Bo Yuan, Longtao Huang, Xinbing Wang, Zhouhan Lin
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Adaptive Attention Span in Transformers (P19-1)
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| Challenge: | We extend the maximum context size of a neural network called Transformer to 8k characters. |
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