MOROCO: The Moldavian and Romanian Dialectal Corpus
Table 6: Accuracy rates (in %) of the KRR based on string kernels for Romanian dialect identification versus Arabic Ali et al. (2016) and German Samardžić et al. (2016) dialect identification, respectively. The results for the Arabic and German dialect identification tasks are taken from our previous work Ionescu and Butnaru (2017). For each corpus, we include the number of dialects (#dialects) and the average number of tokens in each sample (#tokens per sample).
['Corpus', '#dialects', '#tokens', 'Accuracy']
Difficulty with respect to other dialects. In the case of Arabic, we have reached performance levels of around 76% for discriminating between five dialects. In the same time, we have reached performance levels of around 66% for discriminating between four German dialects. However, there are some important differences between these tasks. First of all, the random chance baseline is much high for our binary classification task, as we only have to choose between two dialects: Moldavian or Romanian. Second of all, the number of tokens per sample is much higher for the samples in our corpus compared to the samples provided in the Arabic Ali et al. Before drawing the conclusion that Romanian dialects are easier to discriminate than other dialects, we have to make sure that the experiments are conducted in similar conditions. We leave this discussion for future work.