Looks can be Deceptive: Distinguishing Repetition Disfluency from Reduplication (2025.coling-main)
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| Challenge: | Existing research indicates that disfluencies can constitute up to 5.9% of words in spontaneous speech, with repetitions accounting for over half of these disfluency. |
| Approach: | They propose to use a dataset to analyze reduplication and repetition in speech using computational linguistics to evaluate transformer-based models. |
| Outcome: | The proposed models achieve macro F1 scores of up to 85.62% in Hindi, 83.95% in Telugu, and 84.82% in Marathi for reduplication-repetition classification. |
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| Challenge: | Despite the recent progress in reproducibility, the field is far from reaching a consensus on how reproducibility should be defined, measured and addressed. |
| Approach: | They propose to provide a wide-angle snapshot of current work on reproducibility in NLP. |
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It’s Morphin’ Time! Combating Linguistic Discrimination with Inflectional Perturbations (2020.acl-main)
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| Challenge: | Existing work on societal bias in NLP focuses on race and gender . linguistic background is a unique attribute that has been largely ignored in the field . |
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Understanding the Repeat Curse in Large Language Models from a Feature Perspective (2025.findings-acl)
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| Challenge: | Large language models suffer from repetitive text generation, a phenomenon we refer to as the ”Repeat Curse”. |
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Morphological Inflection: A Reality Check (2023.acl-long)
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| Challenge: | Morphological inflection is a popular task in sub-word NLP with practical and cognitive applications. |
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Generating Repetitions with Appropriate Repeated Words (2022.naacl-main)
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| Challenge: | Existing studies have focused on general response generation with neural network-based approaches, but none have addressed specific types of repetitions. |
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When More Data Hurts: A Troubling Quirk in Developing Broad-Coverage Natural Language Understanding Systems (2022.emnlp-main)
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Elias Stengel-Eskin, Emmanouil Antonios Platanios, Adam Pauls, Sam Thomson, Hao Fang, Benjamin Van Durme, Jason Eisner, Yu Su
| Challenge: | In natural language understanding systems, users’ evolving needs necessitate the addition of new features over time, indexed by new symbols added to the meaning representation space. |
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Data Augmentation for Multiclass Utterance Classification – A Systematic Study (2020.coling-main)
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| Challenge: | a lack of sufficient training data for some categories can cause imbalanced data distributions . a weak classifier may miscategorize a request, resulting in customer dissatisfaction . |
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Why Exposure Bias Matters: An Imitation Learning Perspective of Error Accumulation in Language Generation (2022.findings-acl)
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| Challenge: | Current language generation models suffer from issues such as repetition, incoherence, and hallucinations . |
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Quantifying Compositionality of Classic and State-of-the-Art Embeddings (2025.findings-emnlp)
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| Challenge: | Static word embeddings make strong claims about compositionality, but the SOTA generative models go too far in the other direction. |
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Don’t Augment, Rewrite? Assessing Abusive Language Detection with Synthetic Data (2024.findings-acl)
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| Challenge: | Existing datasets for abusive language detection and content moderation are limited by regulatory bodies and social media platforms. |
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