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:
this https URL

该论文提出了一种简单而有效的策略，通过后验正则化将从事件触发器注释中提取的事件知识纳入主流问答模型中，从而提高面向事件的问答模型的事件推理能力。