用于阿拉伯语方言识别的字符级卷积BiLSTM

WANLP@ACL 2019 Pub Date : 1900-01-01 DOI:10.18653/v1/W19-4636

Mohamed S. Elaraby, A. Zahran

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引用次数: 3

摘要

在本文中，我们描述了CU-RAISA团队对2019Madar共享任务2的贡献，该任务侧重于Twitter用户细粒度方言识别。在参与团队中，我们的系统排名第4(61.54%)-宏观措施。我们的系统使用字符级卷积双向长短期记忆网络进行训练，该网络训练了2k个用户的数据。我们证明了将用户tweet连接起来作为输入的训练进一步优于单独对用户tweet进行训练，并在用户tweet的预测模式上分配用户标签。

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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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WANLP@ACL 2019

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