Papers with neural cognate generation models
On the Robustness of Cognate Generation Models (2022.lrec-1)
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| Challenge: | We examine different types of noise generated by human errors and how these noisy inputs affect the performance of cognate generation models. |
| Approach: | They evaluate two popular neural cognate generation models’ robustness to human-plausible noise. |
| Outcome: | The proposed models are robust to deletion, duplication, swapping, keyboard errors, and a new type of error, phonological errors. |