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        <title>Modeling Nonnative Sentence Processing with L2 Language Models</title>
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            <title>Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing</title>
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            <namePart type="given">Yaser</namePart>
            <namePart type="family">Al-Onaizan</namePart>
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        <name type="personal">
            <namePart type="given">Mohit</namePart>
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            <namePart type="given">Yun-Nung</namePart>
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    <abstract>We study LMs pretrained sequentially on two languages (“L2LMs”) for modeling nonnative sentence processing. In particular, we pretrain GPT2 on 6 different first languages (L1s), followed by English as the second language (L2). We examine the effect of the choice of pretraining L1 on the model’s ability to predict human reading times, evaluating on English readers from a range of L1 backgrounds. Experimental results show that, while all of the LMs’ word surprisals improve prediction of L2 reading times, especially for human L1s distant from English, there is no reliable effect of the choice of L2LM’s L1. We also evaluate the learning trajectory of a monolingual English LM: for predicting L2 as opposed to L1 reading, it peaks much earlier and immediately falls off, possibly mirroring the difference in proficiency between the native and nonnative populations. Lastly, we provide examples of L2LMs’ surprisals, which could potentially generate hypotheses about human L2 reading.</abstract>
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%0 Conference Proceedings
%T Modeling Nonnative Sentence Processing with L2 Language Models
%A Aoyama, Tatsuya
%A Schneider, Nathan
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F aoyama-schneider-2024-modeling
%X We study LMs pretrained sequentially on two languages (“L2LMs”) for modeling nonnative sentence processing. In particular, we pretrain GPT2 on 6 different first languages (L1s), followed by English as the second language (L2). We examine the effect of the choice of pretraining L1 on the model’s ability to predict human reading times, evaluating on English readers from a range of L1 backgrounds. Experimental results show that, while all of the LMs’ word surprisals improve prediction of L2 reading times, especially for human L1s distant from English, there is no reliable effect of the choice of L2LM’s L1. We also evaluate the learning trajectory of a monolingual English LM: for predicting L2 as opposed to L1 reading, it peaks much earlier and immediately falls off, possibly mirroring the difference in proficiency between the native and nonnative populations. Lastly, we provide examples of L2LMs’ surprisals, which could potentially generate hypotheses about human L2 reading.
%U https://aclanthology.org/2024.emnlp-main.283
%P 4927-4940

Markdown (Informal)

[Modeling Nonnative Sentence Processing with L2 Language Models](https://aclanthology.org/2024.emnlp-main.283) (Aoyama & Schneider, EMNLP 2024)

• Modeling Nonnative Sentence Processing with L2 Language Models (Aoyama & Schneider, EMNLP 2024)

ACL

• Tatsuya Aoyama and Nathan Schneider. 2024. Modeling Nonnative Sentence Processing with L2 Language Models. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 4927–4940, Miami, Florida, USA. Association for Computational Linguistics.