Semantic Sentence Composition Reasoning for Multi-Hop Question Answering

• URL: http://arxiv.org/abs/2203.00160v1
• Date: Tue, 1 Mar 2022 00:35:51 GMT
• Title: Semantic Sentence Composition Reasoning for Multi-Hop Question Answering
• Authors: Qianglong Chen
• Abstract summary: We present a semantic sentence composition reasoning approach for a multi-hop question answering task. 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%.
• Score: 1.773120658816994
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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%.

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