论文标题
特征：使用预训练的语言模型生成事件影响
EIGEN: Event Influence GENeration using Pre-trained Language Models
论文作者
论文摘要
关于事件和跟踪其影响的推理对于理解过程至关重要。在本文中，我们提出了特征 - 一种利用预训练的语言模型来产生事件影响的方法，其影响是基于环境，其影响的性质以及推理链中的距离。我们还得出了一个新的数据集，用于研究和评估事件影响生成的方法。在自动化评估指标（通过10个胭脂点）和人类与世代的参考和相关性的亲密关系方面，特征在自动化评估指标方面都优于强大的基准。此外，我们表明该事件影响了特征产生的事件改善了“ what-if”问题答案（WIQA）基准（超过3％F1）的性能，尤其是对于需要背景知识和多跳上推理的问题。
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.
