Papers with meta-learning framework
Tackling Long-Tailed Relations and Uncommon Entities in Knowledge Graph Completion (D19-1)
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| Challenge: | Recent studies have focused on the large proportion of infrequent relations which have been ignored by previous studies. |
| Approach: | They propose a meta-learning framework that aims at handling infrequent relations with few-shot learning and uncommon entities by using textual descriptions. |
| Outcome: | The proposed framework outperforms existing methods when dealing with infrequent relations and uncommon entities. |
Meta-XNLG: A Meta-Learning Approach Based on Language Clustering for Zero-Shot Cross-Lingual Transfer and Generation (2022.findings-acl)
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| Challenge: | Existing approaches to learn shareable structures from low-resource languages are limited in the zero-shot setting. |
| Approach: | They propose a meta-learning framework to learn shareable structures from typologically diverse languages based on meta- learning and language clustering. |
| Outcome: | The proposed framework is able to learn shareable structures from typologically diverse languages with limited annotated data. |
Meta-Learning for Domain Generalization in Semantic Parsing (2021.naacl-main)
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| Challenge: | Existing approaches to parsing use standard supervised learning, but little attention has been given to domain generalization. |
| Approach: | They propose a meta-learning framework which targets zero-shot domain generalization for semantic parsing. |
| Outcome: | The proposed framework significantly boosts parser performance on English and Chinese spider datasets. |
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. |
Meta-Learning Adversarial Domain Adaptation Network for Few-Shot Text Classification (2021.findings-acl)
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| Challenge: | Existing approaches for few-shot text classification rely on exploitation of lexical features and distributional signatures on training data, while neglecting to strengthen the model's ability to adapt to new tasks. |
| Approach: | They propose a meta-learning framework integrated with an adversarial domain adaptation network to improve the model's adaptive ability and generate high-quality text embedding for new classes. |
| Outcome: | The proposed framework outperforms the state-of-the-art models on four datasets and shows clear superiority over existing models. |
On Utilizing Constituent Language Resources to Improve Downstream Tasks in Hinglish (2022.findings-emnlp)
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| Challenge: | Intuitively, Hindi and English corpora should aid improve task performance on code-switched Hindi-English. |
| Approach: | They propose a meta-learning framework that utilizes the labelled resources of the downstream tasks in the constituent languages to improve task performance. |
| Outcome: | The proposed framework improves the performance on downstream tasks on code-switched Hindi-English. |
Learning to Learn to Disambiguate: Meta-Learning for Few-Shot Word Sense Disambiguation (2020.findings-emnlp)
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| Challenge: | Existing methods for word sense disambiguation (WSD) are limited and require large datasets annotated with word senses. |
| Approach: | They propose a meta-learning framework for few-shot word sense disambiguation where the goal is to learn to disambiguate unseen words from only a few labeled instances. |
| Outcome: | The proposed framework is based on a large training dataset and a small number of examples. |
Unsupervised Domain Adaptation for Text Classification via Meta Self-Paced Learning (2022.coling-1)
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| Challenge: | Recent methods addressing unsupervised domain adaptation for textual tasks extracted domain-invariant representations through balancing between multiple objectives to align feature spaces between source and target domains. |
| Approach: | They propose to use meta-learning framework to train a neural network-based self-paced learning procedure in an end-to-end manner. |
| Outcome: | The proposed method significantly improves performance on target domains, surpassing state-of-the-art approaches. |
Towards Low-Resource Automatic Program Repair with Meta-Learning and Pretrained Language Models (2023.emnlp-main)
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| Challenge: | Recent advances in deep learning (DL) based APR models have demonstrated promising results by learning from large-scale bug-fix examples in a data-driven manner. |
| Approach: | They propose a meta-learning framework integrated with code pretrained language models to generate fixes for low-resource bugs with limited training samples. |
| Outcome: | The proposed framework learns better error-specific knowledge from high-resource bugs through efficient first-order meta-learning optimization, which allows for a faster adaptation to the target low-resourced bugs. |
Improving Both Domain Robustness and Domain Adaptability in Machine Translation (2022.coling-1)
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| Challenge: | Existing approaches to domain adaptation for NMT depend on high-quality parallel data. |
| Approach: | They propose a meta-learning framework which improves domain robustness and adaptability . they use a word-level domain mixing model and a domain classifier to integrate it . |
| Outcome: | The proposed approach improves domain robustness and adaptability in seen and unseen domains. |
Learning to Selectively Learn for Weakly-supervised Paraphrase Generation (2021.emnlp-main)
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| Challenge: | Existing approaches to generate paraphrases with weak supervision are limited in real-world scenarios due to the lack of coherent and controllable generated paraphrase. |
| Approach: | They propose a method to generate high-quality paraphrases with weak supervision . they obtain abundant weakly-labeled parallel sentences via retrieval-based pseudo paraphrase expansion . |
| Outcome: | The proposed approach achieves significant improvements over existing methods and is even comparable in performance with supervised state-of-the-arts. |
Learning to Customize Model Structures for Few-shot Dialogue Generation Tasks (2020.acl-main)
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| Challenge: | Existing methods for training generative models with minimal corpus are difficult . fine-tuning distinguishes tasks from parameter perspective but ignores model-structure perspective . |
| Approach: | They propose an algorithm that can customize a unique dialogue model for each task in the few-shot setting. |
| Outcome: | The proposed method outperforms baselines on two datasets in task consistency, response quality, diversity and consistency. |
Meta-CQG: A Meta-Learning Framework for Complex Question Generation over Knowledge Bases (2022.coling-1)
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| Challenge: | Existing methods train one encoder-decoder-based model to fit all questions . however, such a one-size-fits-all strategy may not perform well for complex questions involving multiple KB relations or functional constraints. |
| Approach: | They propose a meta-learning framework for complex question generation over knowledge bases . they propose he meta-trained generator can acquire universal meta-knowledge . |
| Outcome: | The proposed framework can acquire universal and transferable meta-knowledge and quickly adapt to long-tailed samples under different dimensions. |
MAPLE: A Meta-learning Framework for Cross-Prompt Essay Scoring (2026.findings-acl)
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| Challenge: | Current approaches to automate essay scoring (AES) treat each writing task as a separate task, resulting in inconsistent performance. |
| Approach: | They propose a meta-learning framework that leverages prototypical networks to learn transferable representations across different writing prompts. |
| Outcome: | The proposed framework outperforms baseline models on ELLIPSE and ASAP (English) and LAILA (Arabic) on three diverse datasets. |
Meta-Semantics Augmented Few-Shot Relational Learning (2025.emnlp-main)
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| Challenge: | Existing methods for few-shot relational learning on knowledge graphs focus on leveraging specific relational information, but rich semantics inherent in KGs have been overlooked. |
| Approach: | They propose a meta-learning framework that integrates meta-semantics with relational information for few-shot relational learning. |
| Outcome: | Extensive experiments on two real-world KG benchmarks validate the effectiveness of PromptMeta in adapting to new relations with limited supervision. |