# Question Dependent Recurrent Entity Network for Question Answering

Source: [Question Dependent Recurrent Entity Network for Question Answering](<https://www.sota2.com/research/paper/68fa63ba1e209131cea0072c>)

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

Data updated: 2025-10-25T20:48:18.501Z

Authors: Andrea Madotto, Giuseppe Attardi

Published: 2017-07-25T11:34:14.000Z

## Abstract

Question Answering is a task which requires building models capable of providing answers to questions expressed in human language. Full question answering involves some form of reasoning ability. We introduce a neural network architecture for this task, which is a form of $Memory\\ Network$, that recognizes entities and their relations to answers through a focus attention mechanism. Our model is named $Question\\ Dependent\\ Recurrent\\ Entity\\ Network$ and extends $Recurrent\\ Entity\\ Network$ by exploiting aspects of the question during the memorization process. We validate the model on both synthetic and real datasets: the $bAbI$ question answering dataset and the $CNN\\ \\&\\ Daily\\ News$ $reading\\ comprehension$ dataset. In our experiments, the models achieved a State-of-The-Art in the former and competitive results in the latter.

## 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/1707.07922>)

[PDF](<https://arxiv.org/pdf/1707.07922.pdf>)