Online encyclopedias, such as Wikipedia, have been well-developed and
researched in the last two decades. One can find any attributes or other
information of a wiki item on a wiki page edited by a community of volunteers.
However, the traditional text, images and tables can hardly express some
aspects of an wiki item. For example, when we talk about ``Shiba Inu'', one may
care more about ``How to feed it'' or ``How to train it not to protect its
food''. Currently, short-video platforms have become a hallmark in the online
world. Whether you're on TikTok, Instagram, Kuaishou, or YouTube Shorts,
short-video apps have changed how we consume and create content today. Except
for producing short videos for entertainment, we can find more and more authors
sharing insightful knowledge widely across all walks of life. These short
videos, which we call knowledge videos, can easily express any aspects (e.g.
hair or how-to-feed) consumers want to know about an item (e.g. Shiba Inu), and
they can be systematically analyzed and organized like an online encyclopedia.
In this paper, we propose Kuaipedia, a large-scale multi-modal encyclopedia
consisting of items, aspects, and short videos lined to them, which was
extracted from billions of videos of Kuaishou (Kwai), a well-known short-video
platform in China. We first collected items from multiple sources and mined
user-centered aspects from millions of users' queries to build an item-aspect
tree. Then we propose a new task called ``multi-modal item-aspect linking'' as
an expansion of ``entity linking'' to link short videos into item-aspect pairs
and build the whole short-video encyclopedia. Intrinsic evaluations show that
our encyclopedia is of large scale and highly accurate. We also conduct
sufficient extrinsic experiments to show how Kuaipedia can help fundamental
applications such as entity typing and entity linking.

本篇论文提出了 Kuaipedia，一个大规模多模态百科全书，由项、方面和与其相连的短视频组成，并首次提出了一个名为 “多模态项方面链接” 的新任务，以将短视频链接到项目 - 方面对中并建立整个短视频百科全书。

Kuaipedia：大规模多模态短视频百科全书

Kuaipedia: a Large-scale Multi-modal Short-video Encyclopedia

Online encyclopediae like Wikipedia contain large amounts of text that need
frequent corrections and updates. The new information may contradict existing
content in encyclopediae. In this paper, we focus on rewriting such dynamically
changing articles. This is a challenging constrained generation task, as the
output must be consistent with the new information and fit into the rest of the
existing document. To this end, we propose a two-step solution: (1) We identify
and remove the contradicting components in a target text for a given claim,
using a neutralizing stance model; (2) We expand the remaining text to be
consistent with the given claim, using a novel two-encoder sequence-to-sequence
model with copy attention. Applied to a Wikipedia fact update dataset, our
method successfully generates updated sentences for new claims, achieving the
highest SARI score. Furthermore, we demonstrate that generating synthetic data
through such rewritten sentences can successfully augment the FEVER
fact-checking training dataset, leading to a relative error reduction of 13%.

本文提出了使用两阶段方法来重写包含大量文本的在线百科全书，通过识别和去除矛盾组件，并使用一种新颖的双编码器序列到序列模型进行扩展，以生成一致性更新的句子。实验结果表明，该方法成功地生成了新的索赔的更新语句，并通过增加重新编写的句子生成合成数据，从而成功地增加了 FEVER 事实检查培训数据集的相对误差降低了 13%。