TY - GEN
T1 - Development and validation of a corpus for machine humor comprehension
AU - Tseng, Yuen Hsien
AU - Wu, Wun Syuan
AU - Chang, Chia Yueh
AU - Chen, Hsueh Chih
AU - Hsu, Wei Lun
N1 - Publisher Copyright:
© European Language Resources Association (ELRA), licensed under CC-BY-NC
PY - 2020
Y1 - 2020
N2 - This work developed a Chinese humor corpus containing 3,365 jokes collected from over 40 sources. Each joke was labeled with five levels of funniness, eight skill sets of humor, and six dimensions of intent by only one annotator. To validate the manual labels, we trained SVM (Support Vector Machine) and BERT (Bidirectional Encoder Representations from Transformers) with half of the corpus (labeled by one annotator) to predict the skill and intent labels of the other half (labeled by the other annotator). Based on two assumptions that a valid manually labeled corpus should follow, our results showed the validity for the skill and intent labels. As to the funniness label, the validation results showed that the correlation between the corpus label and user feedback rating is marginal, which implies that the funniness level is a harder annotation problem to be solved. The contribution of this work is two folds: 1) a Chinese humor corpus is developed with labels of humor skills, intents, and funniness, which allows machines to learn more intricate humor framing, effect, and amusing level to predict and respond in proper context (https://github.com/SamTseng/Chinese_Humor_MultiLabeled). 2) An approach to verify whether a minimum human labeled corpus is valid or not, which facilitates the validation of low-resource corpora.
AB - This work developed a Chinese humor corpus containing 3,365 jokes collected from over 40 sources. Each joke was labeled with five levels of funniness, eight skill sets of humor, and six dimensions of intent by only one annotator. To validate the manual labels, we trained SVM (Support Vector Machine) and BERT (Bidirectional Encoder Representations from Transformers) with half of the corpus (labeled by one annotator) to predict the skill and intent labels of the other half (labeled by the other annotator). Based on two assumptions that a valid manually labeled corpus should follow, our results showed the validity for the skill and intent labels. As to the funniness label, the validation results showed that the correlation between the corpus label and user feedback rating is marginal, which implies that the funniness level is a harder annotation problem to be solved. The contribution of this work is two folds: 1) a Chinese humor corpus is developed with labels of humor skills, intents, and funniness, which allows machines to learn more intricate humor framing, effect, and amusing level to predict and respond in proper context (https://github.com/SamTseng/Chinese_Humor_MultiLabeled). 2) An approach to verify whether a minimum human labeled corpus is valid or not, which facilitates the validation of low-resource corpora.
KW - Corpus Validation
KW - Humor Corpus
KW - Humor Framing
KW - Humor Intent
KW - Multi-label Classification
KW - Traditional Chinese
UR - http://www.scopus.com/inward/record.url?scp=85096608473&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85096608473&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85096608473
T3 - LREC 2020 - 12th International Conference on Language Resources and Evaluation, Conference Proceedings
SP - 1346
EP - 1352
BT - LREC 2020 - 12th International Conference on Language Resources and Evaluation, Conference Proceedings
A2 - Calzolari, Nicoletta
A2 - Bechet, Frederic
A2 - Blache, Philippe
A2 - Choukri, Khalid
A2 - Cieri, Christopher
A2 - Declerck, Thierry
A2 - Goggi, Sara
A2 - Isahara, Hitoshi
A2 - Maegaard, Bente
A2 - Mariani, Joseph
A2 - Mazo, Helene
A2 - Moreno, Asuncion
A2 - Odijk, Jan
A2 - Piperidis, Stelios
PB - European Language Resources Association (ELRA)
T2 - 12th International Conference on Language Resources and Evaluation, LREC 2020
Y2 - 11 May 2020 through 16 May 2020
ER -