Summary of A Knowledge-injected Curriculum Pretraining Framework For Question Answering, by Xin Lin et al.
A Knowledge-Injected Curriculum Pretraining Framework for Question Answering
by Xin Lin, Tianhuang Su, Zhenya Huang, Shangzi Xue, Haifeng Liu, Enhong Chen
First submitted to arxiv on: 11 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel Knowledge-Injected Curriculum Pretraining (KICP) framework for knowledge-based question answering (KBQA) tasks, which leverages knowledge graphs (KGs) to reason and answer questions. The KICP framework consists of three modules: knowledge injection (KI), knowledge adaptation (KA), and curriculum reasoning (CR). The KI module injects knowledge into a pre-trained language model (LM) by generating KG-centered pretraining corpus, allowing for flexible application. The KA module learns knowledge from the generated corpus while maintaining natural language understanding ability to reduce negative impacts. The CR module constructs three corpora with increasing difficulties and trains the LM in a curriculum manner to enable complex reasoning. The proposed framework is evaluated on four real-world datasets, demonstrating improved performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making computers better at answering questions based on what they know. It uses special kinds of databases called knowledge graphs to help them understand and reason like humans do. The researchers created a new way to train these computers, called KICP, which helps them learn from the knowledge graphs and answer questions more accurately. They tested this method with four real-world datasets and found that it worked better than other methods. This is important because it could be used in applications like virtual assistants or chatbots. |
Keywords
» Artificial intelligence » Language model » Language understanding » Pretraining » Question answering
