# Empirical Study on Deep Learning Models for Question Answering

Source: [Empirical Study on Deep Learning Models for Question Answering](<https://www.sota2.com/research/paper/68fa622b1e209131ce9f7d0e>)

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

Data updated: 2025-10-25T20:13:30.351Z

Authors: Yang Yu, Wei Zhang, Chung-Wei Hang, Bing Xiang, Bowen Zhou

Published: 2015-10-26T16:03:27.000Z

## Abstract

In this paper we explore deep learning models with memory component or attention mechanism for question answering task. We combine and compare three models, Neural Machine Translation, Neural Turing Machine, and Memory Networks for a simulated QA data set. This paper is the first one that uses Neural Machine Translation and Neural Turing Machines for solving QA tasks. Our results suggest that the combination of attention and memory have potential to solve certain QA problem.

## 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/1510.07526>)

[PDF](<https://arxiv.org/pdf/1510.07526.pdf>)