Lifu Huang


Script knowledge is critical for humans to understand the broad daily tasks and routine activities in the world. Recently researchers have explored the large-scale pre-trained language models (PLMs) to perform various script related tasks, such as story generation, temporal ordering of event, future event prediction and so on. However, it's still not well studied in terms of how well the PLMs capture the script knowledge. To answer this question, we design three probing tasks: inclusive sub-event selection, starting sub-event selection and temporal ordering to investigate the capabilities of PLMs with and without fine-tuning. The three probing tasks can be further used to automatically induce a script for each main event given all the possible sub-events. Taking BERT as a case study, by analyzing its performance on script induction as well as each individual probing task, we conclude that the stereotypical temporal knowledge among the sub-events is well captured in BERT, however the inclusive or starting sub-event knowledge is barely encoded.


Lifu Huang

Publication Details

Date of publication:
April 16, 2022
Cornell University
Publication note:

Zijian Jin, Xingyu Zhang, Mo Yu, Lifu Huang: Probing Script Knowledge from Pre-Trained Models. CoRR abs/2204.10176 (2022)