This chapter will present a collective effort to compile a comprehensive repository of accessible Chinese language resources that can be used online, licensed for use, or accessed in published form. The compendium will be presented in three parts according to each language resource’s type of accessibility, which is a direct consequence of the type of relevant information provided for each resource. Within each accessibility type, the resources were then further divided according to the following resource types: integrated resources, corpora, lexical resources, and wordnet/ontology. We believe that this four-way classification system will facilitate intuitive searches. However, this design will make it difficult to search for a resource within the same class due to having to rely on the alphabetic order of the titles of the resources. Lastly, it is important for our readers to bear in mind that such a repository is bound to be incomplete given the scale and distributional nature of the resources and the productivity of new resource construction. We plan to post this compendium online to allow easier access and provide updates in the future.

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 linguisic theory and cognitive science, as well as its interactions with downstream NLP tasks and newly proposed multi-X irony processing perspectives.

Automatic Chinese irony detection is a challenging task, and it has a strong impact on linguistic research. However, Chinese irony detection often lacks labeled benchmark datasets. In this paper, we introduce Ciron, the first Chinese benchmark dataset available for irony detection for machine learning models. Ciron includes more than 8.7K posts, collected from Weibo, a micro blogging platform. Most importantly, Ciron is collected with no pre-conditions to ensure a much wider coverage. Evaluation on seven different machine learning classifiers proves the usefulness of Ciron as an important resource for Chinese irony detection.

In this paper, we present a discussion on the problem in the evaluation of irony detection in Mandarin Chinese, especially due to the difficulties of finding an exhaustive definition and to the current lack of a gold standard for computational models. We describe some preliminary results of our experiments on an irony detection system for Chinese, and analyze examples of irony or other related phenomena that turned out to be challenging for NLP classifiers.