  Recent text generation models are easy to generate relevant and fluent text
for the given text, while lack of causal reasoning ability when we change some
parts of the given text. Counterfactual story rewriting is a recently proposed
task to test the causal reasoning ability for text generation models, which
requires a model to predict the corresponding story ending when the condition
is modified to a counterfactual one. Previous works have shown that the
traditional sequence-to-sequence model cannot well handle this problem, as it
often captures some spurious correlations between the original and
counterfactual endings, instead of the causal relations between conditions and
endings. To address this issue, we propose a sketch-and-customize generation
model guided by the causality implicated in the conditions and endings. In the
sketch stage, a skeleton is extracted by removing words which are conflict to
the counterfactual condition, from the original ending. In the customize stage,
a generation model is used to fill proper words in the skeleton under the
guidance of the counterfactual condition. In this way, the obtained
counterfactual ending is both relevant to the original ending and consistent
with the counterfactual condition. Experimental results show that the proposed
model generates much better endings, as compared with the traditional
sequence-to-sequence model.
