Type JournalArticle
Date 2016-08
Tags language learning, spaced repetition
Journal Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Pages 1848--1858

A Trainable Spaced Repetition Model for Language Learning

A description of the novel model of forgetting used by Duolingo at the time and its advantages over traditional models.

Abstract

We present half-life regression (HLR), a novel model for spaced repetition practice with applications to second language acquisition. HLR combines psycholinguistic theory with modern machine learning techniques, indirectly estimating the “half-life” of a word or concept in a student’s long-term memory. We use data from Duolingo — a popular online language learning application — to fit HLR models, reducing error by 45%+ compared to several baselines at predicting student recall rates. HLR model weights also shed light on which linguistic concepts are systematically challenging for second language learners. Finally, HLR was able to improve Duolingo daily student engagement by 12% in an operational user study.

Name Role
Brendan Meeder Author
Burr Settles Author