Non-Sequential Graph Script Induction via Multimedia Grounding
Yu Zhou Sha Li Manling Li Xudong Lin Shih-Fu Chang Mohit Bansal Heng Ji
Online resources such as WikiHow compile a wide range of scripts for
performing everyday tasks, which can assist models in learning to reason about
procedures. However, the scripts are always presented in a linear manner, which
does not reflect the flexibility displayed by people executing tasks in real
life. For example, in the CrossTask Dataset, 64.5% of consecutive step pairs
are also observed in the reverse order, suggesting their ordering is not fixed.
In addition, each step has an average of 2.56 frequent next steps,
demonstrating "branching". In this paper, we propose the new challenging task
of non-sequential graph script induction, aiming to capture optional and
interchangeable steps in procedural planning. To automate the induction of such
graph scripts for given tasks, we propose to take advantage of loosely aligned
videos of people performing the tasks. In particular, we design a multimodal
framework to ground procedural videos to WikiHow textual steps and thus
transform each video into an observed step path on the latent ground truth
graph script. This key transformation enables us to train a script knowledge
model capable of both generating explicit graph scripts for learnt tasks and
predicting future steps given a partial step sequence. Our best model
outperforms the strongest pure text/vision baselines by 17.52% absolute gains
on F1@3 for next step prediction and 13.8% absolute gains on Acc@1 for partial
sequence completion. Human evaluation shows our model outperforming the WikiHow
linear baseline by 48.76% absolute gains in capturing sequential and
non-sequential step relationships.