A systematic review of question answering systems for non-factoid questions

Abstract

Question Answering (QA) is a field of study addressed to develop automatic methods for answering questions expressed in natural language. Recently, the emergence of the new gen- eration of intelligent assistants, such as Siri, Alexa, and Google Assistant, has intensified the importance of an effective and efficient QA system able to handle questions with dif- ferent complexities. Regarding the type of question to be answered, QA systems have been divided into two sub-areas: (i) factoid questions that require a single fact – e.g., a name of a person or a date, and (ii) non-factoid questions that need a more complex answer – e.g., descriptions, opinions, or explanations. While factoid QA systems have overcome human performance on some benchmarks, automatic systems for answering non-factoid questions remain a challenge and an open research problem. This work provides an overview of recent research addressing non-factoid questions. It focuses on which methods have been applied in each task, the data sets available, challenges and limitations, and possible research direc- tions. From a total of 455 recent studies, we selected 75 papers based on our quality control system and exclusion criteria for an in-depth analysis. This systematic review helped to answer what are the tasks and methods involved in non-factoid, what are the data sets available, what the limitations are, and what is the recommendations for future research.