# Semantic Sentence Composition Reasoning for Multi-Hop Question Answering

Source: [Semantic Sentence Composition Reasoning for Multi-Hop Question Answering](<https://www.sota2.com/research/paper/68fa6bdf1e209131cea30c22>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-28T13:56:51.978Z

Authors: Qianglong Chen

Published: 2022-03-01T00:35:51.000Z

## 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%.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2203.00160>)

[PDF](<https://arxiv.org/pdf/2203.00160.pdf>)