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        <title>Text Retrieval for Language Learners: Graded Vocabulary vs. Open Learner Model</title>
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        <namePart type="given">Yan</namePart>
        <namePart type="family">Yeung</namePart>
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            <title>Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)</title>
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            <namePart type="given">Ruslan</namePart>
            <namePart type="family">Mitkov</namePart>
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            <namePart type="given">Galia</namePart>
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    <abstract>A text retrieval system for language learning returns reading materials at the appropriate difficulty level for the user. The system typically maintains a learner model on the user’s vocabulary knowledge, and identifies texts that best fit the model. As the user’s language proficiency increases, model updates are necessary to retrieve texts with the corresponding lexical complexity. We investigate an open learner model that allows user modification of its content, and evaluate its effectiveness with respect to the amount of user update effort. We compare this model with the graded approach, in which the system returns texts at the optimal grade. When the user makes at least half of the expected updates to the open learner model, simulation results show that it outperforms the graded approach in retrieving texts that fit user preference for new-word density.</abstract>
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        <url>https://aclanthology.org/2021.ranlp-1.91</url>
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        <date>2021-09</date>
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            <start>798</start>
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%0 Conference Proceedings
%T Text Retrieval for Language Learners: Graded Vocabulary vs. Open Learner Model
%A Lee, John
%A Yeung, Chak Yan
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)
%D 2021
%8 September
%I INCOMA Ltd.
%C Held Online
%F lee-yeung-2021-text
%X A text retrieval system for language learning returns reading materials at the appropriate difficulty level for the user. The system typically maintains a learner model on the user’s vocabulary knowledge, and identifies texts that best fit the model. As the user’s language proficiency increases, model updates are necessary to retrieve texts with the corresponding lexical complexity. We investigate an open learner model that allows user modification of its content, and evaluate its effectiveness with respect to the amount of user update effort. We compare this model with the graded approach, in which the system returns texts at the optimal grade. When the user makes at least half of the expected updates to the open learner model, simulation results show that it outperforms the graded approach in retrieving texts that fit user preference for new-word density.
%U https://aclanthology.org/2021.ranlp-1.91
%P 798-804

Markdown (Informal)

[Text Retrieval for Language Learners: Graded Vocabulary vs. Open Learner Model](https://aclanthology.org/2021.ranlp-1.91) (Lee & Yeung, RANLP 2021)

• Text Retrieval for Language Learners: Graded Vocabulary vs. Open Learner Model (Lee & Yeung, RANLP 2021)

ACL

• John Lee and Chak Yan Yeung. 2021. Text Retrieval for Language Learners: Graded Vocabulary vs. Open Learner Model. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021), pages 798–804, Held Online. INCOMA Ltd..