Intelligent Conversational Learning Assistant with Deep Natural Language Understanding. (2019)

Recent advances in virtual assistants point to new ways for human-machine interaction, enabling us to communicate effortlessly with different software and hardware-based agents using natural language. By means of interactive chatting, an intelligent agent can better understand our intentions so as to provide seamless integration of technology into our lives. Currently, voice assistants or chatbots are of high demand in both academics and industries in Taiwan as well as the world. In the research community, the mainstream model relies on statistical machine learning. Although such an approach can be automated in the learning phase, the labor involved in labeling the training data is intensive. Moreover, it is difficult to interpret the parameters in the trained model, which poses a problem when conducting error analysis. Therefore, large commercial systems are constructed via rule-based (RB) methods instead. However, RB methods operate on numerous hand-crafted rules which still require humans to construct. Besides, adding more rules can sometimes create conflicts that require extra cost to resolve, making it difficult to scale RB methods to larger domains. In light of these problems, we propose this research project to bridge the gap between the two models by creating a novel chatbot framework that can understand domain knowledge as well as assist in the human learning process. It can provide commendable conversation capabilities that will boost our prevalence in the world. In our framework, a domain expert can quickly compose scripts for various scenarios. Furthermore, the agent is able to understand and extract important knowledge from documents to generate sentences in order to provide a more natural conversation experience with the user. Ultimately, we hope to provide a performance that surpasses the state-of-the-art technologies in Europe, US, and Japan. In addition, we propose extensions of the chatbot that aim to assist in solving math word problems and essay composition. For math problems, we will design an agent that not only solves but also explains the “how” and “why” of the solution in natural language. Besides, it is able to locate where the user may have difficulty in understanding the reasoning, and supply adequate suggestions. For essay composition, our framework can exceed the level of correcting grammatical errors and present paraphrasing suggestions during composing, so as to broaden the knowledge of the user.



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