Generating Vocabulary Sets for Implicit Language Learning using Masked Language Modeling
Files
Date
2022-01-04
Authors
Contributor
Advisor
Department
Instructor
Depositor
Speaker
Researcher
Consultant
Interviewer
Narrator
Transcriber
Annotator
Journal Title
Journal ISSN
Volume Title
Publisher
Volume
Number/Issue
Starting Page
Ending Page
Alternative Title
Abstract
A well-balanced language curriculum must include both explicit vocabulary learning and implicit vocabulary learning. However, most language learning applications focus on explicit instruction. Students require support with implicit vocabulary learning because they need enough context to guess and acquire new words. Traditional techniques aim to teach students enough vocabulary to comprehend the text, thus enabling them to acquire new words. Despite the wide variety of support for vocabulary learning offered by learning applications today, few offer guidance on how to select an optimal vocabulary study set. This paper proposes a novel method of student modeling with masked language modeling to detect words that are required for comprehension of a text. It explores the efficacy of using deep learning via a pre-trained masked language model to model human reading comprehension and presents a vocabulary study set generation pipeline (VSGP). Promising results show that masked language modeling can be used to model human comprehension and the pipeline produces reasonably sized vocabulary study sets that can be integrated into language learning systems.
Description
Keywords
Text Analytics, language modeling, machine learning, vocabulary learning
Citation
Extent
10 pages
Format
Geographic Location
Time Period
Related To
Proceedings of the 55th Hawaii International Conference on System Sciences
Related To (URI)
Table of Contents
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International
Rights Holder
Local Contexts
Collections
Email libraryada-l@lists.hawaii.edu if you need this content in ADA-compliant format.