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| A Long-term Memory Prediction Model for Language Learning via LSTM |
| YE Junyao1, SU Jingyong1,2, WANG Yaowei2, XU Yong1 |
1.School of Lomputer Science and Technology Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong 518055, China;
2.PengCheng Laboratory, Institute of Vision Intelligence, Shenzhen, Guangdong 518055, China |
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Abstract Spaced repetition is a common mnemonic method in language learning. In order to decide proper review intervals for a desired memory effect, it is necessary to predict the learners’ long-term memory. This paper proposes a long-term memory prediction model for language learning via LSTM. We extract statistical features and sequence features from the memory behavior history of learners. The LSTM is used to learn the memory behavior sequence. The half-life regression model is applied to predict the probability of foreign language learners' recall of words. Upon the 9 billion pieces of real memory behavior data collected for evaluation, the sequence features are revealed more informative than statistical features. Compared with the state-of-the-art models, the error of the proposed LSTM-HLR model is significantly reduced by 50%.
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Received: 14 October 2021
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2022, Vol. 36 
