Encoding Explanatory Knowledge for Zero-shot Science Question Answering
arXiv:2105.05737
Abstract
This paper describes N-XKT (Neural encoding based on eXplanatory Knowledge Transfer), a novel method for the automatic transfer of explanatory knowledge through neural encoding mechanisms. We demonstrate that N-XKT is able to improve accuracy and generalization on science Question Answering (QA). Specifically, by leveraging facts from background explanatory knowledge corpora, the N-XKT model shows a clear improvement on zero-shot QA. Furthermore, we show that N-XKT can be fine-tuned on a target QA dataset, enabling faster convergence and more accurate results. A systematic analysis is conducted to quantitatively analyze the performance of the N-XKT model and the impact of different categories of knowledge on the zero-shot generalization task.
References in corpus (6)
- ConceptNet 5.5: An Open Multilingual Graph of General Knowledge
- Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge
- Question Answering as Global Reasoning over Semantic Abstractions
- A Survey on Explainability in Machine Reading Comprehension
- KG^2: Learning to Reason Science Exam Questions with Contextual Knowledge Graph Embeddings
- Sanity Check: A Strong Alignment and Information Retrieval Baseline for Question Answering