# Automatic Stance Detection Using End-to-End Memory Networks

Source: [Automatic Stance Detection Using End-to-End Memory Networks](<https://www.sota2.com/research/paper/68fa64401e209131cea037b0>)

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

Data updated: 2026-01-04T02:57:30.491Z

Authors: Mitra Mohtarami, Ramy Baly, James Glass, Preslav Nakov, Lluis Marquez, Alessandro Moschitti

Published: 2018-04-20T12:48:10.000Z

## Abstract

We present a novel end-to-end memory network for stance detection, which jointly (i) predicts whether a document agrees, disagrees, discusses or is unrelated with respect to a given target claim, and also (ii) extracts snippets of evidence for that prediction. The network operates at the paragraph level and integrates convolutional and recurrent neural networks, as well as a similarity matrix as part of the overall architecture. The experimental evaluation on the Fake News Challenge dataset shows state-of-the-art performance.

## 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.

- [Stance Detection on FNC-1 (test)](<https://www.sota2.com/research/sota/stance-detection-on-fnc-1-test.md>): Stance Detection; Fake News Challenge FNC-1 (test); Weighted Accuracy: 0.8123.

- [Fake News Stance Detection on FNC 1 (test)](<https://www.sota2.com/research/sota/fake-news-stance-detection-on-fnc-1-test.md>): Fake News Stance Detection; FNC 1 (test); Agree: not recorded.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1804.07581>)

[PDF](<https://arxiv.org/pdf/1804.07581.pdf>)

[Implementation](<https://www.sota2.com/www.fakenewschallenge.org>)