Papers with dropout
Efficient Semi-supervised Consistency Training for Natural Language Understanding (2022.naacl-industry)
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| Challenge: | Manually labeled training data is expensive, noisy, and often scarce . semi-supervised learning methods can be used to improve model performance . |
| Approach: | They explore different methods for consistency training on unlabeled data . they use human paraphrasing, back-translation, and dropout to augment unlabed data. |
| Outcome: | The proposed methods outperform purely supervised learning on unlabeled data. |
SCD: Self-Contrastive Decorrelation of Sentence Embeddings (2022.acl-short)
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| Challenge: | Existing methods for self-supervised learning of representations are based on contrastive learning. |
| Approach: | They propose a self-supervised approach that optimizes a joint decorrelation and self-contrastive objective by leveraging the contrast arising from standard dropout at different rates. |
| Outcome: | The proposed method achieves comparable results with state-of-the-art methods on multiple benchmarks without using contrastive pairs. |
Drop Dropout on Single Epoch Language Model Pretraining (2025.findings-acl)
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| Challenge: | Initial dropout was seen as a breakthrough regularization technique that reduced overfitting, yet single-epoch pretraining tasks common to modern LLMs yield minimal overfit. |
| Approach: | They propose to use dropout during single-epoch pretraining to reduce overfitting in language modeling, morpho-syntax, question answering, and MNLI to improve performance. |
| Outcome: | The results show that dropout is not used in large LLMs and improves performance in language modeling, morpho-syntax, question answering, and MNLI. |
MoSLD: An Extremely Parameter-Efficient Mixture-of-Shared LoRAs for Multi-Task Learning (2025.coling-main)
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| Challenge: | LoRA is a key technique for fine-tuning large pre-trained models, yet its performance in multi-task learning scenarios often falls short. |
| Approach: | They propose a mixture-of-shared-LoRAs model with a dropout strategy . they propose to share the upper projection matrix among different experts . |
| Outcome: | The proposed model exhibits excellent performance in both single-task and multi-task scenarios with robust out-of-domain generalization capabilities. |
Do Neural Topic Models Really Need Dropout? Analysis of the Effect of Dropout in Topic Modeling (2023.eacl-main)
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| Challenge: | Dropout is a regularization trick used to resolve overfitting in large feedforward neural networks, but there is nil analysis of it for unsupervised models and in particular, VAE-based neural topic models. |
| Approach: | They propose to use dropout to solve overfitting problems in unsupervised neural topic models by stochastically dropping out the activation of neurons to prevent complex co-adaptations of feature vectors. |
| Outcome: | The proposed class of neural topic models can be used to improve the quality and predictive performance of the generated topics. |
Differentially Private Representation for NLP: Formal Guarantee and An Empirical Study on Privacy and Fairness (2020.findings-emnlp)
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| Challenge: | Existing approaches to learn text representations can encode private information of the input, thus can be exploited to recover such information with reasonable accuracy. |
| Approach: | They propose a novel approach to preserve privacy of the extracted representation from text by combining differential privacy with dropout. |
| Outcome: | The proposed approach preserves privacy of the extracted representation from text while masking words via dropout can enhance privacy. |
Mixed-effects transformers for hierarchical adaptation (2022.emnlp-main)
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| Challenge: | Language differs dramatically from context to context, but prompting can be ineffective when contexts are sparse, out-of-sample, or extra-textual. |
| Approach: | They propose a mixed-effects transformer approach for learning hierarchically-structured prefixes to account for structured variation in language use. |
| Outcome: | The proposed approach can be extended to transformer-based architectures while generalizing well to unseen contexts. |
Designing and Interpreting Probes with Control Tasks (D19-1)
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| Challenge: | Existing studies on supervised models to predict properties from representations have shown high accuracy on a range of linguistic tasks. |
| Approach: | They propose control tasks which associate word types with random outputs to complement linguistic tasks by construction . they find that popular probes on ELMo representations are not selective . |
| Outcome: | The proposed tasks associate word types with random outputs to complement linguistic tasks. |
UniDrop: A Simple yet Effective Technique to Improve Transformer without Extra Cost (2021.naacl-main)
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| Challenge: | Existing approaches to improve the performance of natural language processing models are over-parameterized and overfitted. |
| Approach: | They propose an approach to integrate dropout techniques into the training of Transformer models. |
| Outcome: | The proposed approach can achieve 1.5 BLEU improvement on IWSLT14 translation tasks and better accuracy for the classification even using strong pre-trained RoBERTa as backbone. |
BERT-based Lexical Substitution (P19-1)
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| Challenge: | Existing approaches to lexical substitution tend to overlook good substitute candidates that are not the synonyms of the target words in the lexicals and fail to take into account the substitution’s influence on the global context of the sentence. |
| Approach: | They propose an end-to-end BERT-based lexical substitution approach which proposes and validates substitute candidates without using annotated data or manually curated resources. |
| Outcome: | The proposed approach performs well in proposing and ranking substitute candidates, achieving the state-of-the-art results in both LS07 and LS14 benchmarks. |
ESimCSE: Enhanced Sample Building Method for Contrastive Learning of Unsupervised Sentence Embedding (2022.coling-1)
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| Challenge: | a new method for learning unsupervised sentence embeddings is proposed . unsup-SimCSE is biased because of the length information encoded into the sentence embeds . |
| Approach: | They propose a new unsupervised sentence embedding method that uses dropout to obtain positive pairs from a pre-trained Transformer encoder. |
| Outcome: | The proposed method outperforms the state-of-the-art unsup-SimCSE on a STS task. |
Domain adaptation for part-of-speech tagging of noisy user-generated text (N19-1)
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| Challenge: | Existing POS taggers for canonical German text achieve good results around 97% accuracy, but when applying these trained models to out-of-domain data the performance decreases drastically. |
| Approach: | They propose a neural network that trains an out-of-domain model on a large newswire corpus and transfers those weights by using them as a prior for a model trained on the target domain. |
| Outcome: | The proposed model achieves a tagging accuracy of slightly over 90%, improving on the previous state of the art for this task. |
Bi-Drop: Enhancing Fine-tuning Generalization via Synchronous sub-net Estimation and Optimization (2023.findings-emnlp)
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| Challenge: | Pretrained language models can be fine-tuned on limited training data, which can overfit and thus diminish performance. |
| Approach: | They propose a fine-tuning strategy that selectively updates model parameters using gradients from various sub-nets dynamically generated by dropout. |
| Outcome: | The proposed method outperforms existing methods on the GLUE benchmark and exhibits excellent generalization ability and robustness for domain transfer, data imbalance, and low-resource scenarios. |
LABO: Towards Learning Optimal Label Regularization via Bi-level Optimization (2023.findings-acl)
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| Challenge: | Existing methods for regularizing deep neural networks rely on weight decay, dropout, batch/layer normalization to converge faster and generalize. |
| Approach: | They propose a framework for training with label regularization which includes conventional LS but can also model instance-specific variants. |
| Outcome: | The proposed approach consistently yields better results than conventional regularization on seven machine translation and three image classification tasks while maintaining training efficiency. |
Noisy Label Regularisation for Textual Regression (2022.coling-1)
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| Challenge: | Existing methods to regularise noisy labels are ineffective in the face of noisy data. |
| Approach: | They propose a method that regularises noisy labels and prevents error propagation from the input layer. |
| Outcome: | The proposed method regularises noisy labels and improves generalisation performance over real-world human-disagreement annotations and randomly-corrupted and data-augmented labels. |
MCPG: A Flexible Multi-Level Controllable Framework for Unsupervised Paraphrase Generation (2022.findings-emnlp)
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| Challenge: | Existing studies on controllable unsupervised paraphrase generation are expensive and require supervised training on large parallel corpora. |
| Approach: | They propose a method for controllable unsupervised paraphrase generation that is flexible to adapt to specific domains without extra training. |
| Outcome: | The proposed method outperforms state-of-the-art unsupervised baselines by a margin. |
R-AT: Regularized Adversarial Training for Natural Language Understanding (2022.findings-emnlp)
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| Challenge: | Currently, adversarial training is a popular and powerful regularization method in the natural language domain. |
| Approach: | They propose to regularize adversarial training via dropout by perturbing word embeddings . they find that R-AT can improve many models by reducing adversariality . |
| Outcome: | The proposed method can reduce the inconsistency between training and testing of models with dropout. |
SimCSE: Simple Contrastive Learning of Sentence Embeddings (2021.emnlp-main)
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| Challenge: | Existing methods for learning universal sentence embeddings are based on unsupervised approaches with only dropout as noise. |
| Approach: | They propose an unsupervised approach that takes an input sentence and predicts itself in a contrastive objective with only standard dropout used as noise. |
| Outcome: | The proposed framework performs on par with previous supervised approaches and can produce superior sentence embeddings from unlabeled or labeled data. |
Sequence Length is a Domain: Length-based Overfitting in Transformer Models (2021.emnlp-main)
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| Challenge: | Current Transformer-based sequence-to-sequence architectures can suffer from overfitting during training. |
| Approach: | They propose to use Transformer-based sequence-to-sequence architectures to overcome overfitting problems when generating very long sequences. |
| Outcome: | The proposed model performs worse on very long sequences than previous approaches on string editing and translation tasks when faced with sequences of length diverging from the length distribution in training data. |
Bridging the Empirical-Theoretical Gap in Neural Network Formal Language Learning Using Minimum Description Length (2024.acl-long)
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| Challenge: | Neural networks offer good approximation to many tasks but fail to reach perfect generalization. |
| Approach: | They propose to use a formal language to test whether a theoretically correct solution is not an optimum of commonly used objectives. |
| Outcome: | The proposed model fails to reach the theoretically correct solution even with regularization techniques. |
Not All Modalities at Once: Dynamic Dropout and Bidirectional Fusion for Robust Multi-modal Knowledge Graph Completion (2026.findings-acl)
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| Challenge: | Existing MKGC methods train with all modalities available, implicitly assuming consistent complementarity . however, this often induces modality dependence and modality competition under heterogeneous noise, which can hinder robust multi-modal fusion and limit overall performance. |
| Approach: | They propose a framework to infer missing links in multimodal knowledge graphs by leveraging structured triples together with auxiliary modalities such as text and images. |
| Outcome: | The proposed framework outperforms baselines and achieves new state-of-the-art results. |
Layer-wise Regularized Dropout for Neural Language Models (2024.lrec-main)
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| Challenge: | Existing methods to regularize dropout are consistency training and dropout is a problem in many pre-trained neural language models. |
| Approach: | They propose a layer-wise regularized dropout technique which regularizes dropout at the output layer using consistency training. |
| Outcome: | The proposed model can be regarded as a "self-distillation" framework, in which each sub-model generated by dropout is the other's "teacher" model and "student" model. |