A Character Level Convolutional BiLSTM for Arabic Dialect Identification
In this paper, we describe CU-RAISA teamcontribution to the 2019Madar shared task2, which focused on Twitter User fine-grained dialect identification.Among par-ticipating teams, our system ranked the4th(with 61.54{\%}) F1-Macro measure.Our sys-tem is trained using a character level convo-lutional bidirectional long-short-term memorynetwork trained on 2k users{'} data. We showthat training on concatenated user tweets asinput is further superior to training on usertweets separately and assign user{'}s label on themode of user{'}s tweets{'} predictions.
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