Papers with confusion network
Zero-shot Learning for Grapheme to Phoneme Conversion with Language Ensemble (2022.findings-acl)
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| Challenge: | Existing work focuses on low-resource and endangered languages with limited training sets. |
| Approach: | They propose a hypothesis set for any unseen target language and combine it with a confusion network to propose 'the most likely hypothesis' they test the approach on over 600 unseened languages and demonstrate it significantly outperforms baselines. |
| Outcome: | The proposed model outperforms baselines on over 600 unseen languages. |