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        <title>An Effective Automated Speaking Assessment Approach to Mitigating Data Scarcity and Imbalanced Distribution</title>
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        <namePart type="given">Tien-Hong</namePart>
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    <abstract>Automated speaking assessment (ASA) typically involves automatic speech recognition (ASR) and hand-crafted feature extraction from the ASR transcript of a learner’s speech. Recently, self-supervised learning (SSL) has shown stellar performance compared to traditional methods. However, SSL-based ASA systems are faced with at least three data-related challenges: limited annotated data, uneven distribution of learner proficiency levels and non-uniform score intervals between different CEFR proficiency levels. To address these challenges, we explore the use of two novel modeling strategies: metric-based classification and loss re-weighting, leveraging distinct SSL-based embedding features. Extensive experimental results on the ICNALE benchmark dataset suggest that our approach can outperform existing strong baselines by a sizable margin, achieving a significant improvement of more than 10% in CEFR prediction accuracy.</abstract>
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%0 Conference Proceedings
%T An Effective Automated Speaking Assessment Approach to Mitigating Data Scarcity and Imbalanced Distribution
%A Lo, Tien-Hong
%A Chao, Fu-An
%A Wu, Tzu-i
%A Sung, Yao-Ting
%A Chen, Berlin
%Y Duh, Kevin
%Y Gomez, Helena
%Y Bethard, Steven
%S Findings of the Association for Computational Linguistics: NAACL 2024
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F lo-etal-2024-effective
%X Automated speaking assessment (ASA) typically involves automatic speech recognition (ASR) and hand-crafted feature extraction from the ASR transcript of a learner’s speech. Recently, self-supervised learning (SSL) has shown stellar performance compared to traditional methods. However, SSL-based ASA systems are faced with at least three data-related challenges: limited annotated data, uneven distribution of learner proficiency levels and non-uniform score intervals between different CEFR proficiency levels. To address these challenges, we explore the use of two novel modeling strategies: metric-based classification and loss re-weighting, leveraging distinct SSL-based embedding features. Extensive experimental results on the ICNALE benchmark dataset suggest that our approach can outperform existing strong baselines by a sizable margin, achieving a significant improvement of more than 10% in CEFR prediction accuracy.
%U https://aclanthology.org/2024.findings-naacl.86
%P 1352-1362

Markdown (Informal)

[An Effective Automated Speaking Assessment Approach to Mitigating Data Scarcity and Imbalanced Distribution](https://aclanthology.org/2024.findings-naacl.86) (Lo et al., Findings 2024)

• An Effective Automated Speaking Assessment Approach to Mitigating Data Scarcity and Imbalanced Distribution (Lo et al., Findings 2024)

ACL

• Tien-Hong Lo, Fu-An Chao, Tzu-i Wu, Yao-Ting Sung, and Berlin Chen. 2024. An Effective Automated Speaking Assessment Approach to Mitigating Data Scarcity and Imbalanced Distribution. In Findings of the Association for Computational Linguistics: NAACL 2024, pages 1352–1362, Mexico City, Mexico. Association for Computational Linguistics.