Improving the Question Answering Quality using Answer Candidate Filtering based on Natural-Language Features

• URL: http://arxiv.org/abs/2112.05452v1
• Date: Fri, 10 Dec 2021 11:09:44 GMT
• Title: Improving the Question Answering Quality using Answer Candidate Filtering based on Natural-Language Features
• Authors: Aleksandr Gashkov, Aleksandr Perevalov, Maria Eltsova, Andreas Both
• Abstract summary: We address the problem of how the Question Answering (QA) quality of a given system can be improved. Our main contribution is an approach capable of identifying wrong answers provided by a QA system. In particular, our approach has shown its potential while removing in many cases the majority of incorrect answers.
• Score: 117.44028458220427
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Software with natural-language user interfaces has an ever-increasing importance. However, the quality of the included Question Answering (QA) functionality is still not sufficient regarding the number of questions that are answered correctly. In our work, we address the research problem of how the QA quality of a given system can be improved just by evaluating the natural-language input (i.e., the user's question) and output (i.e., the system's answer). Our main contribution is an approach capable of identifying wrong answers provided by a QA system. Hence, filtering incorrect answers from a list of answer candidates is leading to a highly improved QA quality. In particular, our approach has shown its potential while removing in many cases the majority of incorrect answers, which increases the QA quality significantly in comparison to the non-filtered output of a system.

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