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arXiv · 1809.07485

A Quantitative Evaluation of Natural Language Question Interpretation for Question Answering Systems

Takuto Asakura·Jin-Dong Kim·Yasunori Yamamoto·Yuka Tateisi·Toshihisa Takagi
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Abstract

Systematic benchmark evaluation plays an important role in the process of improving technologies for Question Answering (QA) systems. While currently there are a number of existing evaluation methods for natural language (NL) QA systems, most of them consider only the final answers, limiting their utility within a black box style evaluation. Herein, we propose a subdivided evaluation approach to enable finer-grained evaluation of QA systems, and present an evaluation tool which targets the NL question (NLQ) interpretation step, an initial step of a QA pipeline. The results of experiments using two public benchmark datasets suggest that we can get a deeper insight about the performance of a QA system using the proposed approach, which should provide a better guidance for improving the systems, than using black box style approaches.

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Takuto Asakura, Jin-Dong Kim, Yasunori Yamamoto, Yuka Tateisi, Toshihisa Takagi. 2018-09-20. A Quantitative Evaluation of Natural Language Question Interpretation for Question Answering Systems. https://arxiv.org/abs/1809.07485

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