Kernekoncepter
K-Act2Emo introduces a specialized Korean commonsense knowledge graph focusing on indirect emotional expressions, enhancing emotion inference models' training effectiveness.
Statistik
In constructing K-Act2Emo, we employ a two-step process. First, we gather indirect emotional expressions through crowdsourcing. Then, we collect corresponding emotions and other inferences through a second round of crowdsourcing.
The acceptance rate for nodes in NegEnv is slightly lower compared to that in PosEnv. The evaluation results indicate an acceptance rate of 82.53% for the dataset.
Citater
"In this study, we compare K-Act2Emo with ATOMIC20 20."
"Kullm We used a 5.8B-sized version of Kullm based on polyglot-ko."