Impact of Domain Adaptation on Cross-Lingual NER Model Transferability

Abstract

Abstract: We propose a method for zero-resource domain adaptation of DNN acoustic models, for use in low-resource situations where the only in-language training data available may be poorly matched to the intended target domain. Our method uses a multi-lingual model in which several DNN layers are shared between languages. This architecture enables domain adaptation transforms learned for one well-resourced language to be applied to an entirely different low-resource language. First, to develop the technique we use English as a well-resourced language and take Spanish to mimic a low-resource language. E

Research Question

What is the impact of domain adaptation techniques on the cross-lingual transferability of teacher-student NER models, measured by F1 score improvements across domain-shifted low-resource language datasets?

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