Papers with competitor models
Global Attention Decoder for Chinese Spelling Error Correction (2021.findings-acl)
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| Challenge: | Existing methods for Chinese spelling error correction focus on local contextual information, thus misleading the user and reducing performance. |
| Approach: | They propose a global attention decoder that learns the global relationship of correct input characters and candidates of potential error characters. |
| Outcome: | The proposed method outperforms all competitor models by a large margin of up to 6.2% on three human-annotated datasets. |
Confusionset-guided Pointer Networks for Chinese Spelling Check (P19-1)
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| Challenge: | Existing methods to detect and fix errors in Chinese are limited due to context. |
| Approach: | They propose a Confusionset-guided pointer network for Chinese Spell Check task . they propose to use off-the-shelf confusionset to guide character generation . |
| Outcome: | The proposed model outperforms all competitor models on three human-annotated datasets. |