Evaluation of Question Answering Systems: Complexity of judging a natural language

• URL: http://arxiv.org/abs/2209.12617v1
• Date: Sat, 10 Sep 2022 12:29:04 GMT
• Title: Evaluation of Question Answering Systems: Complexity of judging a natural language
• Authors: Amer Farea, Zhen Yang, Kien Duong, Nadeesha Perera, and Frank Emmert-Streib
• Abstract summary: Question answering (QA) systems are among the most important and rapidly developing research topics in natural language processing (NLP) This survey attempts to provide a systematic overview of the general framework of QA, QA paradigms, benchmark datasets, and assessment techniques for a quantitative evaluation of QA systems.
• Score: 3.4771957347698583
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Question answering (QA) systems are among the most important and rapidly developing research topics in natural language processing (NLP). A reason, therefore, is that a QA system allows humans to interact more naturally with a machine, e.g., via a virtual assistant or search engine. In the last decades, many QA systems have been proposed to address the requirements of different question-answering tasks. Furthermore, many error scores have been introduced, e.g., based on n-gram matching, word embeddings, or contextual embeddings to measure the performance of a QA system. This survey attempts to provide a systematic overview of the general framework of QA, QA paradigms, benchmark datasets, and assessment techniques for a quantitative evaluation of QA systems. The latter is particularly important because not only is the construction of a QA system complex but also its evaluation. We hypothesize that a reason, therefore, is that the quantitative formalization of human judgment is an open problem.

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