Reasoning about events and tracking their influences is fundamental to understanding processes. In this paper, we present EIGEN - a method to leverage pre-trained language models to generate event influences conditioned on a context, nature of their influence, and the distance in a reasoning chain. We also derive a new dataset for research and evaluation of methods for event influence generation. EIGEN outperforms strong baselines both in terms of automated evaluation metrics (by 10 ROUGE points) and human judgments on closeness to reference and relevance of generations. Furthermore, we show that the event influences generated by EIGEN improve the performance on a "what-if" Question Answering (WIQA) benchmark (over 3% F1), especially for questions that require background knowledge and multi-hop reasoning.

本文提出了一种方法 - EIGEN，以利用预先训练的语言模型来生成事件影响，该方法是在推理链中的一个上下文、影响性质和距离的条件下进行的，并派生了一个用于研究和评估事件影响方法的新数据集。EIGEN在自动评估指标（10 ROUGE点）和人类判断（参考关系的密切程度和相关性）方面均优于强基线。此外，我们还展示了EIGEN生成的事件影响如何提高“如果”问答（WIQA）基准测试的性能（超过3％ F1），特别是对于需要背景知识和多跳推理的问题。

使用预先训练的语言模型生成事件影响