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Effective Architectures for Low Resource Multilingual Named Entity Transliteration
In this paper, we evaluate LSTM, biLSTM, GRU, and Transformer architectures for the task of name transliteration in a many-to-one multilingual paradigm, transliterating from 590 languages to English.
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