# Text-based Question Answering from Information Retrieval and Deep Neural Network Perspectives: A Survey

Source: [Text-based Question Answering from Information Retrieval and Deep Neural Network Perspectives: A Survey](<https://www.sota2.com/research/paper/68fa67941e209131cea1725c>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T22:18:12.808Z

Authors: Zahra Abbasiantaeb, Saeedeh Momtazi

Published: 2020-02-16T16:24:39.000Z

## Abstract

Text-based Question Answering (QA) is a challenging task which aims at finding short concrete answers for users' questions. This line of research has been widely studied with information retrieval techniques and has received increasing attention in recent years by considering deep neural network approaches. Deep learning approaches, which are the main focus of this paper, provide a powerful technique to learn multiple layers of representations and interaction between questions and texts. In this paper, we provide a comprehensive overview of different models proposed for the QA task, including both traditional information retrieval perspective, and more recent deep neural network perspective. We also introduce well-known datasets for the task and present available results from the literature to have a comparison between different techniques.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2002.06612>)

[PDF](<https://arxiv.org/pdf/2002.06612.pdf>)