# Ensemble approach for natural language question answering problem

Source: [Ensemble approach for natural language question answering problem](<https://www.sota2.com/research/paper/68fa66451e209131cea0f634>)

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

Data updated: 2025-10-28T13:56:40.216Z

Authors: Anna Aniol, Marcin Pietron

Published: 2019-08-26T15:01:24.000Z

## Abstract

Machine comprehension, answering a question depending on a given context paragraph is a typical task of Natural Language Understanding. It requires to model complex dependencies existing between the question and the context paragraph. There are many neural network models attempting to solve the problem of question answering. The best models have been selected, studied and compared with each other. All the selected models are based on the neural attention mechanism concept. Additionally, studies on a SQUAD dataset were performed. The subsets of queries were extracted and then each model was analyzed how it deals with specific group of queries. Based on these three model ensemble model was created and tested on SQUAD dataset. It outperforms the best Mnemonic Reader model.

## 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/1908.09720>)

[PDF](<https://arxiv.org/pdf/1908.09720.pdf>)