Papers with meta-learning problem
Self-Supervised Meta-Learning for Few-Shot Natural Language Classification Tasks (2020.emnlp-main)
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| Challenge: | Existing methods for supervised meta-learning require many training tasks to generalize . cloze-style objectives can be used to generate a large, rich, meta-training task distribution from unlabeled text. |
| Approach: | They propose a self-supervised approach to generate a large, rich, meta-learning task distribution from unlabeled text. |
| Outcome: | The proposed approach generates a large, rich, meta-learning task distribution from unlabeled text. |
Prompt-Based Meta-Learning For Few-shot Text Classification (2022.emnlp-main)
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| Challenge: | Existing methods to learn text labels require large amounts of data to build many few-shot tasks. |
| Approach: | They propose a Prompt-Based Meta-Learning model that adds the prompting mechanism to the meta-learning method. |
| Outcome: | The proposed method improves on four text classification datasets with high accuracy and robustness. |
PROTAUGMENT: Unsupervised diverse short-texts paraphrasing for intent detection meta-learning (2021.acl-long)
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| Challenge: | Recent research considers few-shot intent detection as a meta-learning problem because of labeled data scarcity and the number of classes involved. |
| Approach: | They propose a meta-learning algorithm for short texts classification that limits overfitting on the bias introduced by the few-shots classification objective at each episode. |
| Outcome: | The proposed algorithm limits overfitting on the bias introduced by the few-shots classification objective at each episode. |
On the Effectiveness of Sentence Encoding for Intent Detection Meta-Learning (2022.naacl-main)
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| Challenge: | Recent studies on few-shot intent detection have attempted to formulate the task as a meta-learning problem. |
| Approach: | They propose to modify a few-shot intent detection task to produce a non-trivially strong performance without further domain-specific adaptation. |
| Outcome: | The proposed model improves on the prototypical network variants with task-specific fine-tuning. |
Meta-Learning for Low-Resource Neural Machine Translation (D18-1)
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| Challenge: | In this paper, we propose to extend the recently introduced model-agnostic meta-learning algorithm for low-resource neural machine translation (NMT). |
| Approach: | They propose to extend the recently introduced meta-learning algorithm for low-resource neural machine translation (NMT) they frame low-Resource translation as a meta- learning problem where we learn to adapt to low-REsource languages based on multilingual high-resourced language tasks. |
| Outcome: | The proposed meta-learning algorithm outperforms the multilingual, transfer learning based approach and can train a competitive NMT system with only a fraction of training examples. |
Learning to Few-Shot Learn Across Diverse Natural Language Classification Tasks (2020.coling-main)
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| Challenge: | Pre-trained transformer models have shown great success in improving performance on downstream tasks, but fine-tuning on a new task still requires large amounts of labeled data. |
| Approach: | They propose a method which allows optimization-based meta-learning across tasks . they use transformers to train transformer models and find better generalizations . |
| Outcome: | The proposed method outperforms self-supervised training and pre-trained models on 17 NLP tasks. |
SpidR-Adapt: A Universal Speech Representation Model for Few-Shot Adaptation (2026.acl-long)
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Mahi Luthra, Jiayi Shen, Maxime Poli, Angelo Ortiz Tandazo, Yosuke Higuchi, Youssef Benchekroun, Martin Gleize, Charles-Éric Saint-James, Dongyan Lin, Phillip Rust, Angel Villar-Corrales, null Surya, Vanessa Stark, Rashel Moritz, Juan Pino, Yann LeCun, Emmanuel Dupoux
| Challenge: | Empirically, SpidR-Adapt achieves rapid gains in phonemic discriminability and downstream spoken language modeling scores . current self-supervised learning models require thousands of hours of training data to learn meaningful linguistic representations. |
| Approach: | They propose a bi-level optimization framework for rapid adaptation of speech units to new languages using minimal unlabeled data. |
| Outcome: | The proposed model achieves rapid gains in phonemic discriminability and spoken language modeling scores . it surpasses in-domain toplines after training on less than 1h of target-language audio . |