KagNet: Knowledge-Aware Graph Networksfor Commonsense Reasoning
Table 1: Comparisons with large pre-trained language model fine-tuning with different amount of training data.
['[BOLD] Model', '[BOLD] 10(%) of IHtrain IHdev-Acc.(%)', '[BOLD] 10(%) of IHtrain IHtest-Acc.(%)', '[BOLD] 50(%) of IHtrain IHdev-Acc.(%)', '[BOLD] 50(%) of IHtrain IHtest-Acc.(%)', '[BOLD] 100(%) of IHtrain IHdev-Acc.(%)', '[BOLD] 100(%) of IHtrain IHtest-Acc.(%)']
We conduct the experiments with our in-house splits to investigate whether our KagNet can also work well on other universal language encoders (GPT and Bert-Base), particularly with different fractions of the dataset (say 10%, 50%, 100% of the training data). Furthermore, we find that the improvements in a small data situation (10%) is relatively limited, and we believe an important future research direction is thus few-shot learning for commonsense reasoning.