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.
Highlights
IntroductionWhen people try to learn a new language, they learn it through verbal and written communication, including reading, writing, listening, and speaking skills
The conversation is an effective technique for learning a second language [1]
The natural language inference (NLI) task is conducted to investigate the semantic relationship between two sentences
Summary
When people try to learn a new language, they learn it through verbal and written communication, including reading, writing, listening, and speaking skills. The former includes listening and speaking, and the latter includes reading and writing. A partner is mandatory for whatever conversation a learner wants to make It is easy these days to make friends from different countries, cultures, and tribes with the rise of the internet and social media. Learning a second language requires regular practice for a long time It isn’t easy to find someone available for live conversation every day for a long time because of different time-zone, work schedules, and many other reasons. Movies, prerecorded videos, or online courses that provide video lessons can be useful equipment to some extent for understanding a language but not adequate for making conversations
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More From: International Journal of Advanced Computer Science and Applications
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