论文标题
信息图：表作为半结构化数据的推断
INFOTABS: Inference on Tables as Semi-structured Data
论文作者
论文摘要
在本文中，我们观察到半结构化的列表文本无处不在。理解它们不仅需要理解文本片段的含义，还需要它们之间的隐含关系。我们认为，此类数据可以证明是了解我们如何推理信息的测试基础。为了研究这一点，我们介绍了一个名为Infotabs的新数据集，其中包括基于从Wikipedia Info-Boxes提取的前提的人为写的文本假设。我们的分析表明，该处的半结构化，多域和异质性质承认复杂，多方面的推理。实验表明，尽管人类注释者同意桌上 - 假设对之间的关系，但几种标准的建模策略在任务上没有成功，这表明关于表的推理可能会带来困难的建模挑战。
In this paper, we observe that semi-structured tabulated text is ubiquitous; understanding them requires not only comprehending the meaning of text fragments, but also implicit relationships between them. We argue that such data can prove as a testing ground for understanding how we reason about information. To study this, we introduce a new dataset called INFOTABS, comprising of human-written textual hypotheses based on premises that are tables extracted from Wikipedia info-boxes. Our analysis shows that the semi-structured, multi-domain and heterogeneous nature of the premises admits complex, multi-faceted reasoning. Experiments reveal that, while human annotators agree on the relationships between a table-hypothesis pair, several standard modeling strategies are unsuccessful at the task, suggesting that reasoning about tables can pose a difficult modeling challenge.
