KHAN：用于准确政治立场预测的知识感知分层注意力网络
KHAN: Knowledge-Aware Hierarchical Attention Networks for Accurate Political Stance Prediction
AI总结：
本文提出知识感知分层注意力网络KHAN，通过分层注意力网络和知识编码融合文章语境、基调与现实世界实体外部知识，并构建两个独立政治知识图谱，在三个数据集上实现了高准确性的政治立场预测。
摘要：
新闻文章的政治立场预测已被广泛研究，以缓解回音室效应——人们陷入自己的思维并强化其既有信念。以往关于政治立场问题的工作集中在（1）识别能够反映新闻文章政治立场的政治因素，以及（2）有效地捕捉这些因素。尽管它们取得了经验上的成功，但在其识别出的因素对政治立场预测的有效性方面，缺乏充分的论证。受此启发，在本研究中，我们进行了一项用户研究，以调查政治立场预测中的重要因素，并观察到新闻文章的语境和基调（隐式）以及文章中出现的现实世界实体的外部知识（显式）对于确定其政治立场很重要。基于这一观察，我们提出了一种新颖的知识感知政治立场预测方法（KHAN），采用（1）分层注意力网络（HAN）来学习三个不同层级中单词和句子之间的关系，以及（2）知识编码（KE），将现实世界实体的外部知识纳入政治立场预测过程。此外，为了考虑对立政治立场之间微妙且重要的差异，我们自行构建了两个独立的政治知识图谱（KG）（即 KG-lib 和 KG-con），并学习融合不同的政治知识。通过对三个真实世界数据集的广泛评估，我们证明了 DASH 在（1）准确性、（2）效率和（3）有效性方面的优越性。
英文摘要：
The political stance prediction for news articles has been widely studied to mitigate the echo chamber effect -- people fall into their thoughts and reinforce their pre-existing beliefs. The previous works for the political stance problem focus on (1) identifying political factors that could reflect the political stance of a news article and (2) capturing those factors effectively. Despite their empirical successes, they are not sufficiently justified in terms of how effective their identified factors are in the political stance prediction. Motivated by this, in this work, we conduct a user study to investigate important factors in political stance prediction, and observe that the context and tone of a news article (implicit) and external knowledge for real-world entities appearing in the article (explicit) are important in determining its political stance. Based on this observation, we propose a novel knowledge-aware approach to political stance prediction (KHAN), employing (1) hierarchical attention networks (HAN) to learn the relationships among words and sentences in three different levels and (2) knowledge encoding (KE) to incorporate external knowledge for real-world entities into the process of political stance prediction. Also, to take into account the subtle and important difference between opposite political stances, we build two independent political knowledge graphs (KG) (i.e., KG-lib and KG-con) by ourselves and learn to fuse the different political knowledge. Through extensive evaluations on three real-world datasets, we demonstrate the superiority of DASH in terms of (1) accuracy, (2) efficiency, and (3) effectiveness.
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