When is Char Better Than Subword: A Systematic Study of Segmentation Algorithms for Neural Machine Translation (2021.acl-short)
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| Challenge: | Subword segmentation algorithms can produce sub-optimal segmentation when the target language is rich in morphological changes or there is not enough data for learning compact composition rules. |
| Approach: | They compare character-based and subword-based neural machine translation systems . they find character-driven models are better at handling morphological phenomena . |
| Outcome: | The character-based models are better at handling morphological phenomena, generating rare and unknown words, and more suitable for transferring to unseen domains. |
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| Challenge: | Recent work shows that deeper character-based neural machine translation models outperform subword-based models. |
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The Effectiveness of Morphology-aware Segmentation in Low-Resource Neural Machine Translation (2021.eacl-srw)
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| Challenge: | Current NMT systems typically operate at the level of subwords, causing problems of vocabulary sparsity. |
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| Challenge: | Existing approaches to train character-level models require very deep architectures that are difficult and slow to train. |
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| Challenge: | Existing methods for segmenting words into subword units are not robust enough to handle multiple subword candidates. |
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