  Answering questions using pre-trained language models (LMs) and knowledge
graphs (KGs) presents challenges in identifying relevant knowledge and
performing joint reasoning.We compared LMs (fine-tuned for the task) with the
previously published QAGNN method for the Question-answering (QA) objective and
further measured the impact of additional factual context on the QAGNN
performance. The QAGNN method employs LMs to encode QA context and estimate KG
node importance, and effectively update the question choice entity
representations using Graph Neural Networks (GNNs). We further experimented
with enhancing the QA context encoding by incorporating relevant knowledge
facts for the question stem. The models are trained on the OpenbookQA dataset,
which contains ~6000 4-way multiple choice questions and is widely used as a
benchmark for QA tasks. Through our experimentation, we found that
incorporating knowledge facts context led to a significant improvement in
performance. In contrast, the addition of knowledge graphs to language models
resulted in only a modest increase. This suggests that the integration of
contextual knowledge facts may be more impactful for enhancing question
answering performance compared to solely adding knowledge graphs.
