Contextual and Semantic Fusion Network for Multiple-Choice Reading Comprehension

Multiple-choice reading comprehension (MCRC) aims to build an intelligent system that automatically selects an answer from a candidate set when given a passage and a question. Existing MCRC systems rarely consider incorporating external knowledge such as explicit semantic information. In this work, we propose a Contextual and Semantic Fusion Network (CSFN) which effectively integrates contextual and semantic representation. CSFN introduces explicit structured semantics from pre-trained semantic role labeling. Specially, we regard explicit semantic representation as an important feature to fuse with contextual representation, which enriches the representation of sentences. By combining with the transfer learning strategy, the CSFN model has better generalization over limited datasets. To evaluate the ability of our model, we conduct experiments on three MCRC benchmark datasets: RACE, DREAM, and MCTest. Experimental results demonstrate the effectiveness of our proposed model.

Automated summary

The study proposes a Contextual and Semantic Fusion Network (CSFN) that effectively integrates contextual and semantic representation, incorporating explicit structured semantics and transfer learning strategy for better generalization over limited datasets. Experimental results on three MCRC benchmark datasets (RACE, DREAM, MCTest) demonstrate the effectiveness of the proposed model.