Papers with super-low resource setting
TAMS: Translation-Assisted Morphological Segmentation (2024.acl-long)
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| Challenge: | Canonical morphological segmentation is a key task in endangered language documentation . training data for canonical segmentation can be difficult, making it difficult to train high quality models. |
| Approach: | They propose a model that leverages translation data to speed up canonical segmentation . they propose to use translation data as an additional signal to leverage the data . |
| Outcome: | The proposed model outperforms baseline models in a super-low resource setting but yields mixed results on training splits with more data. |