ℂ𝕆𝕄𝔼𝕋: Commonsense Transformers for Automatic Knowledge Graph Construction
Table 4: Effect of amount of training data on automatic evaluation of commonsense generations
['[BOLD] % train data', '[BOLD] PPL', '[BOLD] BLEU-2', '[BOLD] N/T  [ITALIC] o', '[BOLD] N/U  [ITALIC] o']
S4SS2SSS0Px4 Efficiency of learning from seed tuples Because not all domains will have large available commonsense KBs on which to train, we explore how varying the amount of training data available for learning affects the quality and novelty of the knowledge that is produced. Using only 1% of the training data clearly diminishes the quality of the produced generations, with significantly lower observed results across both quality and novelty metrics. Interestingly, we note that training the model without pre-trained weights performs comparably to training with 10% of the seed tuples, quantifying the impact of using pre-trained language representations.