  Sentence order prediction is the task of finding the correct order of
sentences in a randomly ordered document. Correctly ordering the sentences
requires an understanding of coherence with respect to the chronological
sequence of events described in the text. Document-level contextual
understanding and commonsense knowledge centered around these events are often
essential in uncovering this coherence and predicting the exact chronological
order. In this paper, we introduce STaCK -- a framework based on graph neural
networks and temporal commonsense knowledge to model global information and
predict the relative order of sentences. Our graph network accumulates temporal
evidence using knowledge of `past' and `future' and formulates sentence
ordering as a constrained edge classification problem. We report results on
five different datasets, and empirically show that the proposed method is
naturally suitable for order prediction. The implementation of this work is
publicly available at: https://github.com/declare-lab/sentence-ordering.
