Multi-Round Iterative Retrieval Algorithm for Parsing Question-Answering Process
[Objective]This paper designs a retrieval model to explore the interpretability of question-answering tasks.It examines the reasoning processes of these reading comprehension models and improves sentence relevance in traditional unsupervised retrieval algorithms.[Methods]We proposed a new unsupervised retrieval model ISR,which integrated modules of Pearson correlation coefficient,GloVe word embeddings,and IDF weighting.The ISR model conducted fine-grained retrieval of inference sentences through multi-round iterations.[Results]The proposed model's P,R,and F1 metrics were 2.4％,1.8％,and 2.1％higher than the MSSwQ model on the MultiRC dataset.Its P,R,and F1 metrics were 4.8％,2.6％,and 3.7％higher than the MSSwQ on the HotPotQA dataset.[Limitations]There might be excessive matching issues due to the model's retrieval matching mechanism.[Conclusions]The proposed model improves the accuracy of retrieval inference sentences,which can be effectively applied to the question-answering tasks.
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