Papers with low-resource scenario
Handling Noisy Labels for Robustly Learning from Self-Training Data for Low-Resource Sequence Labeling (N19-3)
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| Challenge: | In low-resource environments, self-training is less effective due to unreliable annotations . we combine self-teaching with noise handling to clean the self-labeled data . |
| Approach: | They propose to combine self-training with noise handling to clean unlabeled data . they propose to model clean and noisy labels separately to improve performance . |
| Outcome: | The proposed method performs better than baseline methods on Chunking and NER. |
Named Entity Recognition in Industrial Tables using Tabular Language Models (2022.emnlp-industry)
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| Challenge: | Table transformers are used for encoding tabular data but are not yet used in industrial applications. |
| Approach: | They propose a dedicated table data augmentation strategy based on available domain-specific knowledge graphs to enhance the performance of transformer-based models. |
| Outcome: | The proposed model outperforms baseline models and its inductive bias is vital for convergence of transformer-based models. |
Morphosyntactic Tagging with Pre-trained Language Models for Arabic and its Dialects (2022.findings-acl)
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| Challenge: | Pre-trained morphosyntactic tagging models outperform existing systems in Modern Standard Arabic and all the Arabic dialects studied. |
| Approach: | They present results on morphosyntactic tagging across different varieties of Arabic using pre-trained transformer language models. |
| Outcome: | The proposed models outperform existing systems in Modern Standard Arabic, 2.8% in Gulf, 1.6% in Egyptian, and 8.3% in Levantine. |
DRK: Discriminative Rule-based Knowledge for Relieving Prediction Confusions in Few-shot Relation Extraction (2022.coling-1)
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| Challenge: | Existing methods to identify relation type in low-resource scenario fall into prediction confusions owing to the limited inference ability over shallow text features. |
| Approach: | They propose a discriminative rule-based knowledge method to identify the relation type between entities in a given text in the low-resource scenario. |
| Outcome: | The proposed method improves on four types of meta tasks with a 6.0% accuracy gain on average. |
Deriving Machine Attention from Human Rationales (D18-1)
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| Challenge: | Attention-based models are successful when trained on large amounts of data. |
| Approach: | They propose an approach to map human-annotated rationales to high-performing attention and use this to guide models trained in low-resource scenarios. |
| Outcome: | The proposed model yields over 15% error reduction on benchmark datasets. |
Reasoning Makes Good Annotators : An Automatic Task-specific Rules Distilling Framework for Low-resource Relation Extraction (2023.findings-emnlp)
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| Challenge: | Existing methods to extract knowledge from unlabeled data generate noise labels. |
| Approach: | They propose an automatic task-specific rules distilling framework to generate a logic rule from unlabeled data. |
| Outcome: | The proposed framework could power the labeling ability by discovering reliable model-labeled data. |
FalAI: A Dataset for End-to-end Spoken Language Understanding in a Low-Resource Scenario (2024.lrec-main)
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Andres Pineiro-Martin, Carmen Garcia-Mateo, Laura Docio-Fernandez, Maria del Carmen Lopez-Perez, Jose Gandarela-Rodriguez
| Challenge: | End-to-end (E2E) Spoken Language Understanding systems extract structured information from speech signals using a single model. |
| Approach: | They propose to use a dataset to extract structured information from speech signals . they define splits for noisy audio, hesitant audio and audio where sentence has changed . |
| Outcome: | The proposed model exploits acoustic information and avoids cascading errors . falAI dataset is the largest public SLU dataset in Galician and first to be obtained in low-resource scenario. |