Papers with MAML
Missing Modality meets Meta Sampling (M3S): An Efficient Universal Approach for Multimodal Sentiment Analysis with Missing Modality (2022.aacl-main)
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| Challenge: | Existing methods to address missing modalities often assume a particular modality is completely missing due to recording or transmission error. |
| Approach: | They propose a missing modality-based meta-sampling approach for multimodal sentiment analysis with missing modalities . they conduct experiments on IEMOCAP, SIMS and CMU-MOSI datasets . |
| Outcome: | The proposed method significantly improves on existing models with a mixture of missing modalities. |
Beyond Reptile: Meta-Learned Dot-Product Maximization between Gradients for Improved Single-Task Regularization (2021.findings-emnlp)
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| Challenge: | Existing approaches to improve generalization of neural models use a small component of the gradient for maximizing dot-product between batches. |
| Approach: | They propose to use a finite differences first-order algorithm to calculate a gradient from dot-product of gradients and regularize it. |
| Outcome: | The proposed method outperforms previous approaches of Reptile and MAML when used as a regularization technique. |
Meta-learning via Language Model In-context Tuning (2022.acl-long)
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| Challenge: | Recent advances in large language models have reduced "task learning and prediction" to a simple sequence prediction problem. |
| Approach: | They propose a meta-learning method that recasts task adaptation and prediction as a sequence prediction problem. |
| Outcome: | The proposed method outperforms MAML on two classification tasks and improves on binaryClfs. |
PubMedCLIP: How Much Does CLIP Benefit Visual Question Answering in the Medical Domain? (2023.findings-eacl)
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| Challenge: | Medical visual question answering is a multimodal task that requires a system to understand both medical images and textual questions and infer associations between them. |
| Approach: | They propose a fine-tuned version of CLIP for the medical domain based on PubMed articles. |
| Outcome: | The proposed model improves accuracy up to 3% on two MedVQA benchmark datasets. |
Minimax and Neyman–Pearson Meta-Learning for Outlier Languages (2021.findings-acl)
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| Challenge: | Model-agnostic meta-learning (MAML) is a strategy to learn resource-poor languages in a sample-efficient fashion. |
| Approach: | They propose a model-agnostic meta-learning strategy that minimizes the expected risk across languages with a uniform prior . they propose 'minimax' and 'neyman-pearson' models that constrain the risk in each language to a maximum threshold. |
| Outcome: | The proposed model reduces the maximum risk across languages while constraining the risk in each language to a maximum threshold. |
Investigating Meta-Learning Algorithms for Low-Resource Natural Language Understanding Tasks (D19-1)
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| Challenge: | Existing methods to learn general representations of text can achieve sub-optimal performance in low-resource scenarios. |
| Approach: | They propose to use language model pre-training and multi-task learning to learn robust representations but these methods can achieve sub-optimal performance in low-resource scenarios. |
| Outcome: | The proposed model outperforms strong baselines on the GLUE benchmark and can be adapted to new tasks efficiently and effectively. |
Decomposed Meta-Learning for Few-Shot Named Entity Recognition (2022.findings-acl)
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| Challenge: | Named entity recognition systems aim at recognizing unseen entity types based on a few labeled examples. |
| Approach: | They propose a decomposed meta-learning approach to solve few-shot span detection and few- shot entity typing problems by introducing a model-agnostic meta-loop algorithm. |
| Outcome: | The proposed approach achieves superior performance over prior methods on benchmarks. |
Meta-Information Guided Meta-Learning for Few-Shot Relation Classification (2020.coling-main)
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| Challenge: | Existing meta-learning models rely on implicit instance statistics and are unreliability and weak interpretability. |
| Approach: | They propose a meta-information guided meta-learning framework that uses semantics to guide meta- learning . experimental results demonstrate the effectiveness of the proposed framework . |
| Outcome: | The proposed framework can establish connections between instance-based information and semantic-based data, enabling faster initialization and adaptation. |
AdaptFlow: Adaptive Workflow Optimization via Meta-Learning (2025.findings-emnlp)
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Runchuan Zhu, Bowen Jiang, Lingrui Mei, Fangkai Yang, Lu Wang, Haoxiang Gao, Fengshuo Bai, Pu Zhao, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang
| Challenge: | Existing approaches to large language models rely on static templates or manual workflows. |
| Approach: | AdaptFlow is a language-based meta-learning framework inspired by model-agnostic meta- learning. |
| Outcome: | AdaptFlow outperforms manual and automated workflows on question answering, code generation and mathematical reasoning benchmarks. |
X-METRA-ADA: Cross-lingual Meta-Transfer learning Adaptation to Natural Language Understanding and Question Answering (2021.naacl-main)
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| Challenge: | Multilingual models have gained popularity for their zero-shot cross-lingual transfer learning capabilities, but their generalization ability is inconsistent for typologically diverse languages. |
| Approach: | They propose a meta-learning approach that adapts MAML to learn to adapt to new languages . they extensively evaluate two cross-lingual NLU tasks using English as source and spanish as target . |
| Outcome: | The proposed approach outperforms naive fine-tuning on cross-lingual tasks for most languages. |
Towards Low-Resource Semi-Supervised Dialogue Generation with Meta-Learning (2020.findings-emnlp)
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| Challenge: | Existing systems that use labelled data to generate dialogues are lacking in high accuracy. |
| Approach: | They propose a meta-learning based semi-supervised explicit dialogue state tracker for neural dialogue generation, denoted as MEDST. |
| Outcome: | The proposed system outperforms existing systems by 18.7% goal accuracy and 14.3% entity match rate on the KVRET corpus with 2% labelled data in semi-supervision. |
Towards Cross-Lingual Audio Abuse Detection in Low-Resource Settings with Few-Shot Learning (2025.coling-main)
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| Challenge: | Online abusive content detection, particularly in low-resource settings, remains underexplored. |
| Approach: | They propose to use pre-trained audio representations to detect abusive language in Indian languages using Few Shot Learning (FSL) . |
| Outcome: | The proposed model can be used to classify abusive language in 10 languages using the ADIMA dataset with FSL. |
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. |
Few-Shot Representation Learning for Out-Of-Vocabulary Words (P19-1)
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| Challenge: | Existing methods for learning word embedding assume there are enough occurrences for each word in the corpus to accurately estimate the representation of words. |
| Approach: | They propose to fit a representation function to predict an oracle embedding vector based on limited contexts. |
| Outcome: | The proposed model outperforms existing methods in constructing an accurate embedding for OOV words and improves downstream tasks when the embeddable is utilized. |
Boosting Natural Language Generation from Instructions with Meta-Learning (2022.emnlp-main)
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| Challenge: | Recent work shows that language models trained with multi-task instructional learning (MTIL) can solve diverse NLP tasks in zero-shot settings with improved performance compared to prompt tuning. |
| Approach: | They propose to adapt meta-learning to MTIL in three directions: 1) Model Agnostic Meta Learning (MAML), 2) Hyper-Network adaptation to generate task specific parameters conditioned on instructions. |
| Outcome: | The proposed approaches improve over strong baselines in zero-shot settings and are most impactful when the test tasks are strictly zero- shot and are "hard" |
Personalizing Dialogue Agents via Meta-Learning (P19-1)
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| Challenge: | Existing personalized dialogue models use human designed persona descriptions to improve dialogue consistency. |
| Approach: | They propose to extend Model-Agnostic Meta-Learning (MAML) to personalized dialogue learning without using persona descriptions. |
| Outcome: | The proposed model outperforms baseline models in terms of human-evaluated fluency and consistency on a persona-chat dataset. |
Meta-Learning for Fast Cross-Lingual Adaptation in Dependency Parsing (2022.acl-long)
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Anna Langedijk, Verna Dankers, Phillip Lippe, Sander Bos, Bryan Cardenas Guevara, Helen Yannakoudakis, Ekaterina Shutova
| Challenge: | Meta-learning can help overcome resource scarcity in cross-lingual NLP problems . pre-training of models requires large annotated training sets for the task at hand . |
| Approach: | They propose to use meta-learning to train a model to learn a parameter initialization that can adapt quickly to new languages. |
| Outcome: | The proposed model-agnostic meta-learning improves on language transfer and standard supervised learning baselines for unseen, typologically diverse, and low-resource languages in a few-shot learning setup. |
Meta-Reinforced Multi-Domain State Generator for Dialogue Systems (2020.acl-main)
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| Challenge: | Existing methods to train a multi-domain dialogue state tracker are lacking in accuracy. |
| Approach: | They propose a Meta-Reinforced Multi-Domain State Generator to train a DST meta-learning model with a few domains as source domains and a new domain as target domain. |
| Outcome: | The proposed system outperforms the traditional training approach with extremely little training data in target domain. |
Know Where You’re Going: Meta-Learning for Parameter-Efficient Fine-Tuning (2023.findings-acl)
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| Challenge: | Existing studies on parameter-efficient fine-tuning methods require additional measures after pre-training and before fine-uning. |
| Approach: | They propose to take parameter-efficient fine-tuning into consideration after pre-training and before fine-uning and use meta-learning to prime a model specifically for parameter-efficiency. |
| Outcome: | The proposed method improves on a pre-trained model with certain modifications and achieves 4.96 points on cross-lingual NER fine-tuning. |