Chiung-Yu Chiang

The modern conversational agent requires high-quality datasets, which are often the bottlenecks when building models. This paper introduces MatDC, an entirely human-produced dialogue dataset with full semantic annotations in Chinese. The dataset features linguistic variations given users' intents and fully annotated semantic slots. MatDC dataset was completely human-edited, and the curation comprises two stages. At first, templates design stage, domain editors first construct schemas and compose ten dialogues between the agents and the users based on the back-end database. Secondly, in the dialogue rewrite stage, rewriters generate sentential variations for each template, under the constraints that the normalized slot values are kept unchanged. The underlying methodology of the MatDC is more open to extension and more adaptable to different domains. To demonstrate the applicability of the dataset, we build a dialogue agent with conventional pipeline architecture. We expect the MatDC dataset to provide additional training data and testing ground for dialogue agent studies.

This paper proposes a computational model of idiomaticity for Chinese Quadra-syllabic idiomatic expressions based on variations, compoundness and compositeness measure. Two classification experiments are conducted to test the model, together with linguistic analysis of the connection to wordnet. The result is promising and we believe that it will shed more light on our understanding of cognitive dynamics that underlies multiword expressions processing.

This paper presents a novel word granularity-aware annotation framework for Chinese. Anchored in current functionalist linguistics, this model rearranges the boundary of word segmentation and linguistic annotation, and gears toward a deeper understanding of lexical units and their behavior. The web-based annotation UI also supports flexible annotation tasks for various linguistic and affective phenomena.

With the development of Teaching Chinese as an International Language and the professionalization trend of Chinese learning, legal Chinese becomes more and more important. To support the legal Chinese teaching and provide Chinese learners, Chinese teachers and other legal workers with authentic data, this paper constructs a legal corpus, which contains 35 legal texts of Mainland China. This study automatically segments the texts into words and manually checks all the segmentation results. Besides, through using the quantitative and qualitative analysis methods, this paper analyzes the common vocabulary of legal Chinese, analyzes the features of legal Chinese, compares the differences between the common vocabulary of legal Chinese and that of the international Chinese teaching, and compares the differences of the common meaning between legal Chinese words and common words in international Chinese vocabulary syllabus. This study also makes reference to the classification of Chinese word level in The Syllabus of Chinese Vocabulary and Characters Levels [18] to classify the words in the legal corpus and explores the application of this corpus in international Chinese teaching. This study finds that there are many differences between legal Chinese and general Chinese, in terms of the common vocabulary and the common meaning of words. So, it can be seen that the legal vocabulary has particularity in the teaching. We cannot directly utilize the existing vocabulary teaching methods to the teaching of legal Chinese vocabulary. Therefore, this paper puts forward several solutions for solving this problem.