Google Research

QC-GO Submission for MADAR Shared Task: Arabic Fine-Grained Dialect Identification

  • Ahmed Abdelali
  • Hamdy Mubarak
  • Kareem Darwish
  • Mohamed Eldesouki
  • Mohammed Attia
  • Younes Samih
MADAR Shared on Dialect Identification -- ACL 2019 (2019)

Abstract

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.

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