Meta Fine-Tuning Neural Language Models for Multi-Domain Text Mining
Table 7: Few-shot natural language inference results over MNLI in terms of accuracy (%).
['[BOLD] Genre', '[BOLD] With MFT?  [BOLD] No', '[BOLD] With MFT?  [BOLD] Yes', '[BOLD] Improvement', '[BOLD] With MFT?  [BOLD] No', '[BOLD] With MFT?  [BOLD] Yes', '[BOLD] Improvement', '[BOLD] With MFT?  [BOLD] No', '[BOLD] With MFT?  [BOLD] Yes', '[BOLD] Improvement']
From the experimental results, we can safely come to the following conclusions. i) MFT improves the performance for text mining of all genres in MNLI, regardless of the percentages of the original training sets we use. ii) MFT has a larger impact on smaller training sets (a 3.9% improvement in accuracy for 5% few-shot learning, compared to a 2.6% improvement for 20%). iii) The improvement of applying MFT before fine-tuning is almost the same as doubling the training data size. Therefore, MFT is highly useful for few-shot learning when the training data of other domains are available.