We study the problem of automatic fact-checking, paying special attention to
the impact of contextual and discourse information. We address two related
tasks: (i) detecting check-worthy claims, and (ii) fact-checking claims. We
develop supervised systems based on neural networks, kernel-based support
vector machines, and combinations thereof, which make use of rich input
representations in terms of discourse cues and contextual features. For the
check-worthiness estimation task, we focus on political debates, and we model
the target claim in the context of the full intervention of a participant and
the previous and the following turns in the debate, taking into account
contextual meta information. For the fact-checking task, we focus on answer
verification in a community forum, and we model the veracity of the answer with
respect to the entire question--answer thread in which it occurs as well as
with respect to other related posts from the entire forum. We develop annotated
datasets for both tasks and we run extensive experimental evaluation,
confirming that both types of information ---but especially contextual
features--- play an important role.

本文研究了自动事实检查的问题，关注上下文和语篇信息的影响。研究并解决了两个相关任务：检测与检查有关的声明以及检查有关的声明。开发了基于神经网络、基于内核的支持向量机和两者组合的监督系统，在输入表示方面使用了丰富的话语线索和上下文特征。从政治辩论中着重关注可检查性估计任务，并在辩论的整个干预、前面和后续转变的语境中对目标声明进行建模，考虑语境元信息。从社区论坛中关注答案验证的事实检查任务，并将答案的真实性与其所在的整个问题 - 答案线程以及来自整个论坛的其他相关帖子的真实性进行建模。我们为两个任务开发了注释数据集，并进行了广泛的实验评估，确认两种信息 (尤其是上下文特征) 发挥重要作用。

使用上下文和语篇信息进行自动事实检查

Automatic Fact-Checking Using Context and Discourse Information

Text-level discourse parsing aims to unmask how two sentences in the text are
related to each other. We propose the task of Visual Discourse Parsing, which
requires understanding discourse relations among scenes in a video. Here we use
the term scene to refer to a subset of video frames that can better summarize
the video. In order to collect a dataset for learning discourse cues from
videos, one needs to manually identify the scenes from a large pool of video
frames and then annotate the discourse relations between them. This is clearly
a time consuming, expensive and tedious task. In this work, we propose an
approach to identify discourse cues from the videos without the need to
explicitly identify and annotate the scenes. We also present a novel dataset
containing 310 videos and the corresponding discourse cues to evaluate our
approach. We believe that many of the multi-discipline AI problems such as
Visual Dialog and Visual Storytelling would greatly benefit from the use of
visual discourse cues.

本文提出 Visual Discourse Parsing 任务，通过识别视频场景来了解视频中场景之间的话语关系，并提出了一种不需要显式身份识别和注释场景的方法识别视觉话语线索。通过构建包含 310 个视频和相应话语线索的新数据集评估所提出的方法，这可能有助于诸如 Visual Dialog 和 Visual Storytelling 等领域的多学科 AI 问题的解决。