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An AI Framework to Teach English as a Foreign Language: CSIEC

AI Magazine

Its multiple functions-- including grammar-based gap-filling exercises, scenario show, free chatting, and chatting on a given topic--can satisfy the various requirements for students with different backgrounds and learning abilities. After a brief explanation of the conception of the dialogue system, as well as a survey of related works, I will illustrate the system structure and describe its pedagogical functions with the underlying AI techniques, such as natural language processing and rulebased reasoning, in detail. I will summarize the free Internet usage within a six-month period and its integration into English classes in universities and middle schools. The evaluation findings about the class integration show that the chatting function has been improved and frequently utilized by users, and the application of the CSIEC system on English instruction can motivate learners to practice English and enhance their learning process. Finally, I will conclude with potential improvements.


The Generation of Textual Entailment with NLML in an Intelligent Dialogue system for Language Learning CSIEC

arXiv.org Artificial Intelligence

This research report introduces the generation of textual entailment within the project CSIEC (Computer Simulation in Educational Communication), an interactive web-based human-computer dialogue system with natural language for English instruction. The generation of textual entailment (GTE) is critical to the further improvement of CSIEC project. Up to now we have found few literatures related with GTE. Simulating the process that a human being learns English as a foreign language we explore our naive approach to tackle the GTE problem and its algorithm within the framework of CSIEC, i.e. rule annotation in NLML, pattern recognition (matching), and entailment transformation. The time and space complexity of our algorithm is tested with some entailment examples. Further works include the rules annotation based on the English textbooks and a GUI interface for normal users to edit the entailment rules.


An AI Framework to Teach English as a Foreign Language: CSIEC

AI Magazine

CSIEC (Computer Simulation in Educational Communication), is not only an intelligent web-based human-computer dialogue system with natural language for English instruction, but also a learning assessment system for learners and teachers. Its multiple functions--including grammar-based gap filling exercises, scenario show, free chatting and chatting on a given topic--can satisfy the various requirements for students with different backgrounds and learning abilities. We will summarize the free Internet usage within a six month period and its integration into English classes in universities and middle schools. The evaluation findings about the class integration show that the chatting function has been improved and frequently utilized by the users, and the application of the CSIEC system on English instruction can motivate the learners to practice English and enhance their learning process.


Dealing with Trouble: A Data-Driven Model of a Repair Type for a Conversational Agent

AAAI Conferences

Troubles in hearing, comprehension or speech production are common in human conversations, especially if participants of the conversation communicate in a foreign language that they have not yet fully mastered. Here I describe a data-driven model for simulation of dialogue sequences where the learner user does not understand the talk of a conversational agent in chat and asks for clarification.


Machine Learning from Conversation with Humans

AAAI Conferences

Human social learning is an effective process that has inspired many existing machine learning techniques, such as learning from observation and learning by demonstration. Hence, in this paper, we are proposing another form of social learning, Learning from a Conversation (LfC). LfC is an open-ended machine learning system in which an artificially intelligent agent learns from extended dialog with a human. Our system enables the agent to adapt to new changes based on the human input. We provide a detailed description of our system and report its performance by providing several examples that reflect our system's efficiency. Test results indicate that the prototype was successful in learning from conversation.