Multi-task Learning for Low-resource Second Language Acquisition Modeling
This addresses the challenge of personalized learning systems for second language learners in data-scarce environments, representing an incremental advance over existing methods.
The paper tackles the problem of second language acquisition modeling in low-resource scenarios by proposing a multi-task learning method that leverages common patterns across language-learning datasets, resulting in significantly better performance than state-of-the-art baselines in low-resource settings and slight improvements in non-low-resource scenarios.
Second language acquisition (SLA) modeling is to predict whether second language learners could correctly answer the questions according to what they have learned. It is a fundamental building block of the personalized learning system and has attracted more and more attention recently. However, as far as we know, almost all existing methods cannot work well in low-resource scenarios due to lacking of training data. Fortunately, there are some latent common patterns among different language-learning tasks, which gives us an opportunity to solve the low-resource SLA modeling problem. Inspired by this idea, in this paper, we propose a novel SLA modeling method, which learns the latent common patterns among different language-learning datasets by multi-task learning and are further applied to improving the prediction performance in low-resource scenarios. Extensive experiments show that the proposed method performs much better than the state-of-the-art baselines in the low-resource scenario. Meanwhile, it also obtains improvement slightly in the non-low-resource scenario.