Papers with deep learning-based methods

3 papers
Automatic Readability Assessment for Closely Related Languages (2023.findings-acl)

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Challenge: In recent years, the main focus of research on automatic readability assessment (ARA) has shifted towards using expensive deep learning-based methods with the primary goal of increasing models’ accuracy.
Approach: They focus on how linguistic aspects such as mutual intelligibility or degree of language relatedness can improve ARA in a low-resource setting.
Outcome: The inclusion of CrossNGO, a novel feature exploiting n-gram overlap, significantly improves the performance of ARA models compared to the use of off-the-shelf large multilingual language models alone.
MLAlgo-Bench: Can Machines Implement Machine Learning Algorithms? (2025.findings-emnlp)

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Challenge: Currently, the top-performing models achieve a 48.8% task completion rate on realizing machine learning algorithms .
Approach: They propose a benchmark to test machine learning's ability to generate ML code for humans . they propose an automatic evaluation framework with metrics such as task pass rate and time overhead .
Outcome: The proposed benchmark is unique in its focus on interpreting complex human instructions and producing multi-step, high-complexity code.
GCML: Gradient Coherence Guided Meta-Learning for Cross-Domain Emerging Topic Rumor Detection (2025.emnlp-main)

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Challenge: Existing domain adaptation rumor detection methods ignore the data generalization differences and rely on a large amount of unlabeled target domain samples to achieve domain adaptation.
Approach: They propose a Gradient Coherence guided Meta-Learning approach for emerging topics rumor detection that selectively learns more "generalizable" tasks that are more beneficial in adapting to the target domain.
Outcome: The proposed method outperforms baselines on real-world datasets and significantly outperformed traditional methods on the in-domain condition.

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