We present a conditional text generation framework that posits sentential
expressions of possible causes and effects. This framework depends on two novel
resources we develop in the course of this work: a very large-scale collection
of English sentences expressing causal patterns CausalBank; and a refinement
over previous work on constructing large lexical causal knowledge graphs Cause
Effect Graph. Further, we extend prior work in lexically-constrained decoding
to support disjunctive positive constraints. Human assessment confirms that our
approach gives high-quality and diverse outputs. Finally, we use CausalBank to
perform continued training of an encoder supporting a recent state-of-the-art
model for causal reasoning, leading to a 3-point improvement on the COPA
challenge set, with no change in model architecture.

本研究提出了一种基于条件的文本生成框架，依赖于 CausalBank 和 Cause Effect Graph 两个新资源开发，并使用词汇约束解码来支持析取正约束，利用 CausalBank 对最近一种最先进的因果推理模型的编码器进行持续训练，在不改变模型架构的情况下在 COPA 挑战集上取得了 3 分的提高。