Localization of Fake News Detection via Multitask Transfer Learning
Table 4: Consolidated experiment results. The first section shows finetuning results for base transfer learning methods and the baseline siamese network. The second section shows results for ULMFiT without Language Model Finetuning. The last section shows finetuning results for transformer methods augmented with multitasking heads. BERT and GPT-2 were finetuned for three epochs in all cases and ULMFiT was finetuned for 5 during classifier finetuning.
['Model', 'Val. Accuracy', 'Loss', 'Val. Loss', 'Pretraining Time', 'Finetuning Time']
BERT achieved a final accuracy of 91.20%, now marginally comparable to ULMFiT's full performance. GPT-2, on the other hand, finetuned to a final accuracy of 96.28%, a full 4.69% improvement over the performance of ULMFiT. [CONTINUE] Rersults for this experiment are outlined in Table 4.