• Corpus ID: 44098100

KG^2: Learning to Reason Science Exam Questions with Contextual Knowledge Graph Embeddings

@article{Zhang2018KG2LT,
  title={KG^2: Learning to Reason Science Exam Questions with Contextual Knowledge Graph Embeddings},
  author={Yuyu Zhang and Hanjun Dai and Kamil Toraman and Le Song},
  journal={ArXiv},
  year={2018},
  volume={abs/1805.12393}
}

• Yuyu Zhang, H. Dai, +1 author Le Song
• Published 31 May 2018
• Computer Science
• ArXiv

The AI2 Reasoning Challenge (ARC), a new benchmark dataset for question answering (QA) has been recently released. ARC only contains natural science questions authored for human exams, which are hard to answer and require advanced logic reasoning. On the ARC Challenge Set, existing state-of-the-art QA systems fail to significantly outperform random baseline, reflecting the difficult nature of this task. In this paper, we propose a novel framework for answering science exam questions, which mimics human solving process in an open-book exam. To address the reasoning challenge, we construct contextual knowledge graphs respectively for the question itself and supporting sentences. Our model learns to reason with neural embeddings of both knowledge graphs. Experiments on the ARC Challenge Set show that our model outperforms the previous state-of-the-art QA systems.