Papers with low-resource problem
Pretrain-KGE: Learning Knowledge Representation from Pretrained Language Models (2020.findings-emnlp)
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| Challenge: | Existing knowledge graph embedding models suffer from limited knowledge representation due to sparse and noisy dataset annotations. |
| Approach: | They propose to use pretrained language models to enhance knowledge representation by leveraging world knowledge from pretrained models. |
| Outcome: | Extensive experiments show that the proposed framework can improve results over existing models. |
Cross-Lingual Contrastive Learning for Fine-Grained Entity Typing for Low-Resource Languages (2022.acl-long)
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| Challenge: | Experimental results show that by applying our framework, we can easily learn effective FGET models for low-resource languages. |
| Approach: | They propose a cross-lingual contrastive learning framework to learn FGET models for low-resource languages. |
| Outcome: | The proposed framework can learn effective FGET models for low-resource languages even without human-labeled data. |
GDA: Grammar-based Data Augmentation for Text Classification using Slot Information (2023.findings-emnlp)
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| Challenge: | Recent studies suggest data augmentation approaches to resolve the low-resource problem in natural language processing tasks. |
| Approach: | They propose to use slot information to augment sentences using a set of injective relations between a sentence’s semantics and its syntactical structure to augment the dataset. |
| Outcome: | The proposed approach outperforms all other data augmentation methods by 19.38%. |
Cross-Lingual Cross-Target Stance Detection with Dual Knowledge Distillation Framework (2023.emnlp-main)
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| Challenge: | Existing studies on stance detection were conducted mainly in English due to the low-resource problem in most non-English languages. |
| Approach: | They propose to use a cross-lingual teacher and a teacher to transfer knowledge from source to target language to bridge the discrepancy between languages. |
| Outcome: | The proposed framework bridges the discrepancy between languages and generalizes the knowledge to unseen targets in target language. |
Adaptive LoRA Merge with Parameter Pruning for Low-Resource Generation (2025.findings-acl)
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| Challenge: | Existing methods for adapting LLMs to low-resource tasks keep LoRA parameters frozen and the low-level problem out of their scope. |
| Approach: | They propose a LoRA merge method that updates and prunes LoRA parameters through fine-tuning with minimal target task data. |
| Outcome: | The proposed method improves performance on a low-resource language generation task and improves on previous methods. |
CoCo: Coherence-Enhanced Machine-Generated Text Detection Under Low Resource With Contrastive Learning (2023.emnlp-main)
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| Challenge: | Recent proposed methods fail to consider the linguistic structure of texts and lack the ability to handle the low-resource problem. |
| Approach: | They propose a coherence-based contrastive learning model named CoCo to detect MGTs under low-resource scenario. |
| Outcome: | The proposed model outperforms state-of-the-art methods on two datasets and two self-constructed datasets. |
Zero-shot Cross-lingual Automated Essay Scoring (2024.lrec-main)
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| Challenge: | Existing approaches to automate essay scoring (AES) use pre-trained multilingual representations and writing quality alignment to score essays in unseen languages. |
| Approach: | They propose a novel cross-lingual scoring method using pretrained multilingual representation and writing quality alignment to represent multilingual essays. |
| Outcome: | The proposed method achieves state-of-the-art cross-lingual scoring performance. |