UnibucKernel荣获：连续第二年获得阿拉伯语方言识别第一名
UnibucKernel Reloaded: First Place in Arabic Dialect Identification for the Second Year in a Row
摘要：
我们提出了一种机器学习方法，该方法在2018年VarDial评估活动的阿拉伯语方言识别（ADI）封闭共享任务中排名第一。所提出的方法结合了多个内核使用多核学习。虽然我们的大多数内核都是基于从语音或音标中提取的字符p-gram（也称为n-gram），但我们也使用基于组织者从音频记录中生成的方言嵌入的内核。在学习阶段，我们独立地使用核判别分析（KDA）和核岭回归（KRR）。初步实验表明，KRR提供了更好的分类结果。我们的方法是肤浅和简单的，但在2018年ADI封闭共享任务中获得的经验结果证明，它实现了最佳性能。此外，根据组织者进行的统计显著性检验，我们的宏观F1得分（58.92%）明显优于2018年ADI共享任务中的第二好得分（57.59%）。尽管如此，我们使用组织者在比赛结束后发布的音频嵌入获得了更好的赛后成绩（62.28%的宏观F1分数）。使用非常相似的方法（不包括语音特征），我们在2017年VarDial评估活动的ADI封闭共享任务中排名第一，超过第二名4.62%。因此，我们得出结论，我们的多核学习方法是迄今为止阿拉伯语方言识别的最佳方法。
英文摘要：
We present a machine learning approach that ranked on the first place in the Arabic Dialect Identification (ADI) Closed Shared Tasks of the 2018 VarDial Evaluation Campaign. The proposed approach combines several kernels using multiple kernel learning. While most of our kernels are based on character p-grams (also known as n-grams) extracted from speech or phonetic transcripts, we also use a kernel based on dialectal embeddings generated from audio recordings by the organizers. In the learning stage, we independently employ Kernel Discriminant Analysis (KDA) and Kernel Ridge Regression (KRR). Preliminary experiments indicate that KRR provides better classification results. Our approach is shallow and simple, but the empirical results obtained in the 2018 ADI Closed Shared Task prove that it achieves the best performance. Furthermore, our top macro-F1 score (58.92%) is significantly better than the second best score (57.59%) in the 2018 ADI Shared Task, according to the statistical significance test performed by the organizers. Nevertheless, we obtain even better post-competition results (a macro-F1 score of 62.28%) using the audio embeddings released by the organizers after the competition. With a very similar approach (that did not include phonetic features), we also ranked first in the ADI Closed Shared Tasks of the 2017 VarDial Evaluation Campaign, surpassing the second best method by 4.62%. We therefore conclude that our multiple kernel learning method is the best approach to date for Arabic dialect identification.
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