  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.
