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Mixture of multiple language forms in spoken Chinese is a common but unfavorable issue.. It increases the difficulty of intent understanding and leads to inconvenience for information communication. Existing studies on intent recognition mainly focus on single language form or parallel multilingual language while paying little attention to spoken texts including multiple language forms. In considering that it is hard to capture the semantics of an expression with multiple language forms, it is important to study the problem. To solve this issue, a text representation model for the spoken Chinese expression mixed with English and Chinese Pinyin is proposed. And the feature matrix is built to mine the composition information of English and Pinyin. Besides, the model can efficiently distinguish English from Chinese Pinyin even though both fragments are composed of English letters. Meanwhile, it can effectively process the problem of hidden text information since the problem has been transformed into the Chinese translation task of English and Pinyin. In addition, to verify the performance of the model, the texts processed by this model are used as the input of the classifier. extensive experiments on a large online logistics manual customer service corpus show that this text representation model is correct and effective. It can not only eliminate the obstacles of the mixing of multiple language forms but also bring better results for intent understanding.

Predicting the roles of participants in conversations is a fundamental task to build a system that provides assessment results and feedback for each participant. Various role recognition models have been proposed. Nonetheless, most studies have only utilized verbal or nonverbal features even though people usually express what they think or feel with the combination of language, gestures, and tone of voice. In this paper, we aim to realize a high-performance role recognition model by combining features from various modalities. We design nonverbal features that can be extracted from video and audio data. Then, we construct a multimodal leader identification method that fuses nonverbal features proposed by us and verbal features proposed by a previous study. In our experiments, our multimodal model outperforms the baseline model that utilizes only verbal features. We also conduct some analysis, such as statistical tests and ablation studies, and verify the effectiveness of each modality and feature. In the end, we build a prototype of a feedback system and demonstrate how our study can be applied to the discussion assessment/feedback systems.

Physically, information carriers are encountered in two occurrences, either in native form as physical structures, or in arbitrarily coded, symbolic form such as signal systems or sequences of signs. The symbolic form may rigorously be associated with the existence of life. In contrast, structural information may be present in various physical processes or structures independent of life. The self-organised emergence of symbolic information from structural information may be called ritualisation. A century ago, Julian Huxley had introduced this term in behavioural biology. Subsequently, this evolutionary key process of the emergence of animal and social communication was studied in depth by Konrad Lorenz, Günter Tembrock and other ethologists. Ritualisation exhibits typical features of kinetic phase transitions of the 2^nd kind. From a more general viewpoint, the origin of life, the appearance of human languages and the emergence of human social categories such as money can also be understood as ritualisation transitions. Occurring at some stage of evolutionary history, these transitions have in common that after the crossover, arbitrary symbols are issued and recognised by information-processing devices, by transmitters and receivers in the sense of Shannon’s information theory. In this paper, general properties of the ritualisation transition and the related code symmetry are described. These properties are demonstrated by tutorial examples of very different such transitions in natural, social and technical evolution, reviewed from the perspective of the emergence of symbolic information and its structural historicity.