We propose a simple yet effective strategy to incorporate event knowledge extracted from event trigger annotations via posterior regularization to improve the event reasoning capability of mainstream question-answering (QA) models for event-centric QA. In particular, we define event-related knowledge constraints based on the event trigger annotations in the QA datasets, and subsequently use them to regularize the posterior answer output probabilities from the backbone pre-trained language models used in the QA setting. We explore two different posterior regularization strategies for extractive and generative QA separately. For extractive QA, the sentence-level event knowledge constraint is defined by assessing if a sentence contains an answer event or not, which is later used to modify the answer span extraction probability. For generative QA, the token-level event knowledge constraint is defined by comparing the generated token from the backbone language model with the answer event in order to introduce a reward or penalty term, which essentially adjusts the answer generative probability indirectly. We conduct experiments on two event-centric QA datasets, TORQUE and ESTER. The results show that our proposed approach can effectively inject event knowledge into existing pre-trained language models and achieves strong performance compared to existing QA models in answer evaluation. Code and models can be found: https://github.com/LuJunru/EventQAviaPR.

翻译：我们提出了一种简单而有效的策略，通过后验正则化将事件触发标注中提取的事件知识融入主流问答模型，以提升其在事件中心问答中的事件推理能力。具体而言，我们基于问答数据集中的事件触发标注定义了事件相关知识约束，并利用这些约束对主干预训练语言模型在问答场景中的后验答案输出概率进行正则化。我们分别针对抽取式问答和生成式问答探索了两种不同的后验正则化策略。对于抽取式问答，句级事件知识约束通过评估句子是否包含答案事件来定义，随后用于修正答案跨度提取概率。对于生成式问答，词级事件知识约束通过比较主干语言模型生成的词元与答案事件来定义，以引入奖励或惩罚项，从而间接调整答案生成概率。我们在两个事件中心问答数据集TORQUE和ESTER上进行了实验。结果表明，我们提出的方法能够有效将事件知识注入现有预训练语言模型，并在答案评估中取得了优于现有问答模型的强劲性能。代码和模型可在以下链接获取：https://github.com/LuJunru/EventQAviaPR。