SKR-QA: Semantic ranking and knowledge revise for multi-choice question answering

Knowledge has long been cosnsidered a crucial part of natural language understanding. Many knowledge bases have been constructed, but none of them will ever be complete. Nevertheless, we argue that complete knowledge already exists in natural language. Most previous work on question answering retrieved such knowledge using traditional statistical methods, and consequently, the knowledge retrieved contained co-occurring phrases and could not provide guidance for model understanding and reasoning. Therefore, in addition to demonstrating the effectiveness of natural language knowledge in machine understanding, this study presents a novel knowledge retrieval approach that evaluates the importance of knowledge from a semantic perspective. Furthermore, we propose a knowledge revise mechanism that allows the model to revise the retrieved knowledge from local and global perspectives. We demonstrate our Semantic-rank-and-Knowledge-Revise-based Question Answering (SKR-QA) approach on two challenging multi-choice question and answering tasks: ARC–Challenge and OpenbookQA. Compared with the previous State-of-the-Art (SOTA) models, our work achieves consistent improvements. Moreover, the knowledge obtained by our method is more conducive to machine understanding, thus providing certain interpretability.