Science Question Answering using Instructional Materials
Mrinmaya Sachan, Avinava Dubey, Eric P. Xing

TL;DR
This paper introduces a unified max-margin framework that leverages instructional materials to improve elementary science question answering by uncovering hidden answer structures, leading to superior performance over existing methods.
Contribution
The paper presents a novel max-margin learning approach that models hidden answer structures using instructional materials for elementary science questions.
Findings
Outperforms several strong baseline methods
Effectively models hidden answer structures
Achieves higher accuracy on elementary science test questions
Abstract
We provide a solution for elementary science test using instructional materials. We posit that there is a hidden structure that explains the correctness of an answer given the question and instructional materials and present a unified max-margin framework that learns to find these hidden structures (given a corpus of question-answer pairs and instructional materials), and uses what it learns to answer novel elementary science questions. Our evaluation shows that our framework outperforms several strong baselines.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
