The identification and classification of political claims is an important
step in the analysis of political newspaper reports; however, resources for
this task are few and far between. This paper explores different strategies for
the cross-lingual projection of political claims analysis. We conduct
experiments on a German dataset, DebateNet2.0, covering the policy debate
sparked by the 2015 refugee crisis. Our evaluation involves two tasks (claim
identification and categorization), three languages (German, English, and
French) and two methods (machine translation -- the best method in our
experiments -- and multilingual embeddings).

通过不同的策略进行政治要求分析的跨语言投射是一项重要工作，本文以德国数据集 DebateNet2.0 为基础，涵盖了 2015 年难民危机引发的政策辩论，通过两个任务（要求识别和分类）、三种语言（德语、英语和法语）以及两种方法（机器翻译 —— 实验中的最佳方法和多语言嵌入）进行了实验和评估。

多语言环境下的政治主张识别与分类：初步实验

Political claim identification and categorization in a multilingual  setting: First experiments

Evidence retrieval is a core part of automatic fact-checking. Prior work
makes simplifying assumptions in retrieval that depart from real-world use
cases: either no access to evidence, access to evidence curated by a human
fact-checker, or access to evidence available long after the claim has been
made. In this work, we present the first fully automated pipeline to check
real-world claims by retrieving raw evidence from the web. We restrict our
retriever to only search documents available prior to the claim's making,
modeling the realistic scenario where an emerging claim needs to be checked.
Our pipeline includes five components: claim decomposition, raw document
retrieval, fine-grained evidence retrieval, claim-focused summarization, and
veracity judgment. We conduct experiments on complex political claims in the
ClaimDecomp dataset and show that the aggregated evidence produced by our
pipeline improves veracity judgments. Human evaluation finds the evidence
summary produced by our system is reliable (it does not hallucinate
information) and relevant to answering key questions about a claim, suggesting
that it can assist fact-checkers even when it cannot surface a complete
evidence set.

本文提出了一种完全自动化的流水线，通过从 Web 检索原始证据来检查实际世界中的索赔，结果表明，我们的流水线可以提供可靠且相关的证据摘要，从而帮助事实检查员甄别真假索赔。

从野外获取证据进行的复杂索赔验证

Complex Claim Verification with Evidence Retrieved in the Wild

We present an overview of the CLEF-2018 CheckThat! Lab on Automatic
Identification and Verification of Political Claims, with focus on Task 1:
Check-Worthiness. The task asks to predict which claims in a political debate
should be prioritized for fact-checking. In particular, given a debate or a
political speech, the goal was to produce a ranked list of its sentences based
on their worthiness for fact checking. We offered the task in both English and
Arabic, based on debates from the 2016 US Presidential Campaign, as well as on
some speeches during and after the campaign. A total of 30 teams registered to
participate in the Lab and seven teams actually submitted systems for Task~1.
The most successful approaches used by the participants relied on recurrent and
multi-layer neural networks, as well as on combinations of distributional
representations, on matchings claims' vocabulary against lexicons, and on
measures of syntactic dependency. The best systems achieved mean average
precision of 0.18 and 0.15 on the English and on the Arabic test datasets,
respectively. This leaves large room for further improvement, and thus we
release all datasets and the scoring scripts, which should enable further
research in check-worthiness estimation.

该研究展示了 CLEF-2018 CheckThat！自动识别和验证政治宣言的实验室概述，其中的任务 1 集中在值得检查的问题上；任务的目标是根据是否值得检查，生成一个关于陈述句的排序列表。最成功的方法包括使用多层神经网络和基于分布式表示的组合方法；最佳系统在英文和阿拉伯文测试数据集上的平均精度分别为 0.18 和 0.15。