Second Language Acquisition Modeling: An Ensemble Approach

Osika, Anton; Nilsson, Sofia; Sydorchuk, Andrii; Şahin, Faruk; Huss, Anders

doi:10.48550/arxiv.1806.04525

Cited by 1 publication

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“…• GBDT Here, we use NYU's method [8], which is the best method among all tree ensemble methods. It uses • RNN Here, we use singsound's method [31], which is the best method among all sequence modeling methods. It uses an RNN architecture which has four types of encoders, representing different types of features: token context, linguistic information, user data, and exercise format.…”

Section: A Datasets and Settingsmentioning

confidence: 99%

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Multi-task Learning for Low-Resource Second Language Acquisition Modeling

Huang

Lan

et al. 2020

Web and Big Data

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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 lowresource scenarios because lacking of training data. Fortunately, there are some latent common patterns among different languagelearning tasks, which gives us an opportunity to solve the lowresource 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.

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Section: A Datasets and Settingsmentioning

confidence: 99%

“…2 Here, we add a new baseline GBDT+RNN. This is SanaLabs's method [31] which combines the prediction of a GBDT and an RNN, and it is also the current best method on the 2018 public SLA modeling challenge.…”

Section: E Experiments In the Non-low-resource Scenariomentioning

confidence: 99%

Multi-task Learning for Low-Resource Second Language Acquisition Modeling

Huang

Lan

et al. 2020

Web and Big Data

View full text Add to dashboard Cite

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Second Language Acquisition Modeling: An Ensemble Approach

Cited by 1 publication

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Multi-task Learning for Low-Resource Second Language Acquisition Modeling

Multi-task Learning for Low-Resource Second Language Acquisition Modeling

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