In this paper, we report improved results of the Fake News Challenge Stage 1
(FNC-1) stance detection task. This gain in performance is due to the
generalization power of large language models based on Transformer
architecture, invented, trained and publicly released over the last two years.
Specifically (1) we improved the FNC-1 best performing model adding BERT
sentence embedding of input sequences as a model feature, (2) we fine-tuned
BERT, XLNet, and RoBERTa transformers on FNC-1 extended dataset and obtained
state-of-the-art results on FNC-1 task.

本研究利用 Transformer 架構的 BERT、XLNet 和 RoBERTa 進行 Fake News Challenge 中的立場檢測，將 BERT 句子嵌入應用到模型中，取得了最佳表現和狀態下的成果。

从 Transformer 到 Fake News Challenge 立场检测的迁移学习 (FNC-1) 任务

Transfer Learning from Transformers to Fake News Challenge Stance  Detection (FNC-1) Task

The 2017 Fake News Challenge Stage 1 (FNC-1) shared task addressed a stance
classification task as a crucial first step towards detecting fake news. To
date, there is no in-depth analysis paper to critically discuss FNC-1's
experimental setup, reproduce the results, and draw conclusions for
next-generation stance classification methods. In this paper, we provide such
an in-depth analysis for the three top-performing systems. We first find that
FNC-1's proposed evaluation metric favors the majority class, which can be
easily classified, and thus overestimates the true discriminative power of the
methods. Therefore, we propose a new F1-based metric yielding a changed system
ranking. Next, we compare the features and architectures used, which leads to a
novel feature-rich stacked LSTM model that performs on par with the best
systems, but is superior in predicting minority classes. To understand the
methods' ability to generalize, we derive a new dataset and perform both
in-domain and cross-domain experiments. Our qualitative and quantitative study
helps interpreting the original FNC-1 scores and understand which features help
improving performance and why. Our new dataset and all source code used during
the reproduction study are publicly available for future research.

本研究深入分析了 2017 年虚假新闻挑战的实验设置，提出了适用于少数类别的新的评估度量标准，并提出了一个表现与最佳系统相当的新型特征丰富的堆叠 LSTM 模型，同时对其在领域内和领域间的推广效果进行了定量和定性的研究。

针对虚假新闻挑战赛立场检测任务的回顾性分析

A Retrospective Analysis of the Fake News Challenge Stance Detection  Task

Identifying public misinformation is a complicated and challenging task. An
important part of checking the veracity of a specific claim is to evaluate the
stance different news sources take towards the assertion. Automatic stance
evaluation, i.e. stance detection, would arguably facilitate the process of
fact checking. In this paper, we present our stance detection system which
claimed third place in Stage 1 of the Fake News Challenge. Despite our
straightforward approach, our system performs at a competitive level with the
complex ensembles of the top two winning teams. We therefore propose our system
as the 'simple but tough-to-beat baseline' for the Fake News Challenge stance
detection task.

该论文介绍了一个能够识别公众错误信息、帮助事实核查的简单实用的立场判断系统，它在 Fake News Challenge 的第一阶段中获得第三名，与前两名获胜队伍的复杂集成方法相比表现竞争力，作者将其提议为 Fake News Challenge 立场检测任务的简单但强大的基线。