A Knowledge-Injected Curriculum Pretraining Framework for Question Answering

arXiv:2403.09712v1 Announce Type: new
Abstract: Knowledge-based question answering (KBQA) is a key task in NLP research, and also an approach to access the web data and knowledge, which requires exploiting knowledge graphs (KGs) for reasoning. In the literature, one promising solution for KBQA is to incorporate the pretrained language model (LM) with KGs by generating KG-centered pretraining corpus, which has shown its superiority. However, these methods often depend on specific techniques and resources to work, which may not always be …

abstract arxiv cs.ai cs.cl curriculum data framework graphs key knowledge knowledge graphs language language model literature nlp pretrained language model pretraining question question answering reasoning research solution type web