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RRQA: reconfirmed reader for open-domain question answering

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Abstract

In open-domain question answering (QA), the system needs to answer questions from various fields and forms according to given passages. Machine reading comprehension (MRC) can assist the system in comprehending passages and questions, hence often used in improving the performance of the QA system. More passages will bring more features to the result of comprehension, making the reading result more accurate. However, there is still a lack of effective ways when facing multiple passages because it is difficult to use the effective information brought by multiple passages while processing noisy information. This study introduces a new MRC model called RRQA (Reconfirmed Reader for Open-Domain Question Answering) to integrate the QA scenario into traditional MRC, which focuses on the interaction between questions and passages, considering the noisy information. The proposed model consists of two parts: answer extraction and answer verification. In the part of answer extraction, a multi-layer neural network model extracts candidate answers from each passage according to the question, taking into account that the question may have no answer. In the answer verification part, the final answer is verified by combining the features of all candidate answers, which can reduce the incompleteness of the independent answer extraction. Experiments show that the model RRQA outperforms the state-of-the-art models on the WebQA dataset.

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Acknowledgements

This work is supported by the Fundamental Research Funds for the Central Universities (Grant No. 2572019BH03).

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  1. School of Information and Computer Engineering, Northeast Forestry University, Wenzheng Street, Harbin, 150006, Heilongjiang Province, China

    Shi Li & Wenqian Zhang

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  1. Shi Li
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  2. Wenqian Zhang
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Correspondence to Wenqian Zhang.

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Li, S., Zhang, W. RRQA: reconfirmed reader for open-domain question answering. Appl Intell 53, 18420–18430 (2023). https://doi.org/10.1007/s10489-023-04461-3

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