Automatic Recognition of Speech Acts in Classroom Interaction Based on Multi-Text Classification

The traditional coding process requires mechanical observation and categorization of the various utterances produced in the classroom. Both the judgment and the professionalism of education of the coders are very challenging. With the development of Automatic Speech Recognition (ASR) and natural language processing (NLP). It is possible for researchers to automate the recognition of speech acts in the classroom. There are also many related studies, but they have not been able to complete the automatic recognition of the classroom interaction speech act(CISA). In order to solve problems, our research proposes a practical CISA coding system. And according to this system, a related CISA dataset is established. A Multi-text classification(MTC) model called Bert-TextConcat is proposed for training on the constructed dataset. The trained model performs automatic classification of CISA while referring to the above. After experiments, We demonstrate the effectiveness of the BertTextConcat model and CISA coding systems.