An Empirical Study of Factors Affecting Language-Independent Models
Table 4: Accuracy results of Sentiment Analysis on low-resource languages. We compare the performance of zero-shot transfer learning in LIM=H (without any annotated data from the target languages) and low-resource transfer training in LIM-M (only 4K annotated data from the target languages were used in training).
['Language', 'LIM-H', 'LIM-M']
4.2.2 Results on Low-Resource Languages For low-resource languages LIM-H demonstrates the effects of zero-shot transfer learning on low-resource languages, with an average of 60% accuracy. Since we do not use any low-resource training data in LIM-H, this shows that LIM can be used to address the cold-start problem, where no initial model is available for a new target low-resource language, when building such models from scratch is costly. Furthermore, LIM-M demonstrates how much improvement a LIM can gain by adding only a small amount of data in low-resource languages. In particular, by adding 4K annotated data in each low-resource language, we obtain an average of 11% improvement. This largely saves the cost and time for acquiring annotated data of a new target low-resource language by transferring the knowledge learned from a larger amount of annotated data available in high-resource languages.