Abstract
A language tutoring tool (LTT) helps learning a language through casual human-like conversations. Natural language understanding (NLU) and natural language generation (NLG) are two key components of an LTT. In this paper, we propose a paraphrase detection algorithm that is used as the building block of the NLU. Our proposed tree-LSTM with a self-attention method for paraphrase detection shows accuracy of 87% with a lower parameter of 6.5m, which is much robust and lighter than the other existing paraphrase detection algorithms. Furthermore, we discuss an LTT prototype using the proposed algorithm with having some featured components like- message analysis, grammar detection, dialogue management, and response generation component. Each component is discussed in detail in the methodology section of this paper.