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        <title>Ling-CL: Understanding NLP Models through Linguistic Curricula</title>
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            <title>Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing</title>
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            <namePart type="given">Houda</namePart>
            <namePart type="family">Bouamor</namePart>
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        <name type="personal">
            <namePart type="given">Juan</namePart>
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        <name type="personal">
            <namePart type="given">Kalika</namePart>
            <namePart type="family">Bali</namePart>
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    <abstract>We employ a characterization of linguistic complexity from psycholinguistic and language acquisition research to develop data-driven curricula to understand the underlying linguistic knowledge that models learn to address NLP tasks. The novelty of our approach is in the development of linguistic curricula derived from data, existing knowledge about linguistic complexity, and model behavior during training. Through the evaluation of several benchmark NLP datasets, our curriculum learning approaches identify sets of linguistic metrics (indices) that inform the challenges and reasoning required to address each task. Our work will inform future research in all NLP areas, allowing linguistic complexity to be considered early in the research and development process. In addition, our work prompts an examination of gold standards and fair evaluation in NLP.</abstract>
    <identifier type="citekey">elgaar-amiri-2023-ling</identifier>
    <identifier type="doi">10.18653/v1/2023.emnlp-main.834</identifier>
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%0 Conference Proceedings
%T Ling-CL: Understanding NLP Models through Linguistic Curricula
%A Elgaar, Mohamed
%A Amiri, Hadi
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F elgaar-amiri-2023-ling
%X We employ a characterization of linguistic complexity from psycholinguistic and language acquisition research to develop data-driven curricula to understand the underlying linguistic knowledge that models learn to address NLP tasks. The novelty of our approach is in the development of linguistic curricula derived from data, existing knowledge about linguistic complexity, and model behavior during training. Through the evaluation of several benchmark NLP datasets, our curriculum learning approaches identify sets of linguistic metrics (indices) that inform the challenges and reasoning required to address each task. Our work will inform future research in all NLP areas, allowing linguistic complexity to be considered early in the research and development process. In addition, our work prompts an examination of gold standards and fair evaluation in NLP.
%R 10.18653/v1/2023.emnlp-main.834
%U https://aclanthology.org/2023.emnlp-main.834/
%U https://doi.org/10.18653/v1/2023.emnlp-main.834
%P 13526-13542

Markdown (Informal)

[Ling-CL: Understanding NLP Models through Linguistic Curricula](https://aclanthology.org/2023.emnlp-main.834/) (Elgaar & Amiri, EMNLP 2023)

• Ling-CL: Understanding NLP Models through Linguistic Curricula (Elgaar & Amiri, EMNLP 2023)

ACL

• Mohamed Elgaar and Hadi Amiri. 2023. Ling-CL: Understanding NLP Models through Linguistic Curricula. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 13526–13542, Singapore. Association for Computational Linguistics.