QC-GO Submission for MADAR Shared Task: Arabic Fine-Grained Dialect Identification
QC-GO Submission for MADAR Shared Task: Arabic Fine-Grained Dialect Identification
复制标题
MADAR 共享任务的 QC-GO 提交：阿拉伯语细粒度方言识别
DOI：
--
复制
发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Kareem Darwish
中科院分区：
文献类型：
--
作者：
Younes Samih;Hamdy Mubarak;Ahmed Abdelali;Mohammed Attia;Mohamed I. Eldesouki;Kareem Darwish
本文描述了QC-GO团队提交给MADAR共享任务子任务1（旅游领域方言识别）和子任务2（Twitter用户位置识别）的情况。在参与这两个子任务的过程中，我们探索了许多方法和系统组合，以获得两个任务的最佳性能。其中包括深度神经网络和神经网络。由于个别办法有各种缺点，不同办法的结合能够填补其中的一些空白。我们的系统在子任务1和2的开发集上分别实现了66.1%和67.0%的F1分数。
This paper describes the QC-GO team submission to the MADAR Shared Task Subtask 1 (travel domain dialect identification) and Subtask 2 (Twitter user location identification). In our participation in both subtasks, we explored a number of approaches and system combinations to obtain the best performance for both tasks. These include deep neural nets and heuristics. Since individual approaches suffer from various shortcomings, the combination of different approaches was able to fill some of these gaps. Our system achieves F1-Scores of 66.1% and 67.0% on the development sets for Subtasks 1 and 2 respectively.
DOI：
10.18653/v1/k17-1043
发表时间：
2017-08
期刊：
--
影响因子：
--
作者：
Younes Samih;Mohamed I. Eldesouki;Mohammed Attia;Kareem Darwish;Ahmed Abdelali;Hamdy Mubarak;Laura Kallmeyer
通讯作者：
Younes Samih;Mohamed I. Eldesouki;Mohammed Attia;Kareem Darwish;Ahmed Abdelali;Hamdy Mubarak;Laura Kallmeyer