While Word-level Quality Estimation QE system can provide a way to curtail the over-correction, a significant performance gain has not been observed thus far by utilizing existing APE and QE combination strategies. Another critical issue concerns the inclusion of sensitive personal information within training datasets. In high-ambiguity scenarios, performance varies considerably among different LLMs; for instance, the Llama2 series achieves an RtA of Our findings are consistent across languages. This paper proposes a novel language-agnostic weakly-supervised deep cognate detection framework for under-resourced languages using morphological knowledge from closely related languages.
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