ℂ𝕆𝕄𝔼𝕋: Commonsense Transformers for Automatic Knowledge Graph Construction
Table 11: Automatic evaluations of quality and novelty for generations of Atomic commonsense that are trained with the training set split along different relation types. The training splits are outlined in Table 9.
['[BOLD] Model', '[BOLD] PPL33footnotemark: 3', '[BOLD] BLEU-2', '[BOLD] N/T  [ITALIC] sro44footnotemark: 4', '[BOLD] N/T  [ITALIC] o', '[BOLD] N/U  [ITALIC] o']
For the Atomic corpus, we experiment with multiple multi-task training setups, similar to Sap et al. We also experiment with various information-sharing dataset configurations that organize different relations across common dimensions. For ConceptNet, all models are always trained on all relation types jointly. Because there did not seem to be significant differences between these performances and that of ℂ𝕆𝕄𝔼𝕋 - Full, we did not run additional experiments on these ablations.