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

TL;DR

This paper presents a method to improve question answering systems by filtering answer candidates using natural-language features, significantly increasing answer correctness.

Contribution

It introduces a novel approach for identifying and filtering incorrect answers based on natural-language input and output features, enhancing QA quality.

Findings

Majority of incorrect answers are filtered out

QA quality significantly improved

Approach effective across different QA systems

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.