Semantic Sentence Composition Reasoning for Multi-Hop Question Answering
This addresses the problem of insufficient data for multi-hop QA systems, offering a domain-specific solution.
The paper tackles the challenge of retrieving relevant supporting facts for multi-hop open domain question answering by introducing a semantic sentence composition reasoning approach, which improves SOTA on the QASC task by about 9%.
Due to the lack of insufficient data, existing multi-hop open domain question answering systems require to effectively find out relevant supporting facts according to each question. To alleviate the challenges of semantic factual sentences retrieval and multi-hop context expansion, we present a semantic sentence composition reasoning approach for a multi-hop question answering task, which consists of two key modules: a multi-stage semantic matching module (MSSM) and a factual sentence composition module (FSC). With the combination of factual sentences and multi-stage semantic retrieval, our approach can provide more comprehensive contextual information for model training and reasoning. Experimental results demonstrate our model is able to incorporate existing pre-trained language models and outperform the existing SOTA method on the QASC task with an improvement of about 9%.