Irony is a ubiquitous figurative language in daily communication. Previously,
many researchers have approached irony from linguistic, cognitive science, and
computational aspects. Recently, some progress have been witnessed in automatic
irony processing due to the rapid development in deep neural models in natural
language processing (NLP). In this paper, we will provide a comprehensive
overview of computational irony, insights from linguistic theory and cognitive
science, as well as its interactions with downstream NLP tasks and newly
proposed multi-X irony processing perspectives.

本文综述了计算机语言学中讽刺的研究进展，以及从语言学和认知科学角度的深入解读，同时还提出了讽刺与下游 NLP 任务和多重 X 讽刺处理视角的交互作用。

自动讽刺处理的调研：语言，认知和多元视角

A Survey in Automatic Irony Processing: Linguistic, Cognitive, and Multi-X Perspectives

We propose a general formulation, called Multi-X, for multi-class
multi-instance model fitting - the problem of interpreting the input data as a
mixture of noisy observations originating from multiple instances of multiple
classes. We extend the commonly used alpha-expansion-based technique with a new
move in the label space. The move replaces a set of labels with the
corresponding density mode in the model parameter domain, thus achieving fast
and robust optimization. Key optimization parameters like the bandwidth of the
mode seeking are set automatically within the algorithm. Considering that a
group of outliers may form spatially coherent structures in the data, we
propose a cross-validation-based technique removing statistically insignificant
instances. Multi-X outperforms significantly the state-of-the-art on publicly
available datasets for diverse problems: multiple plane and rigid motion
detection; motion segmentation; simultaneous plane and cylinder fitting; circle
and line fitting.

本文提出一种名为 Multi-X 的多分类多实例模型拟合通用公式，通过一种新的标签空间移动技术，在模型参数的密度分布区域内自动设定关键的优化参数以实现快速且鲁棒的优化。并且结合基于交叉验证的技术去除数据中的离群点。该公式在公开数据集中的多种问题的表现均优于现有技术。