Transfer Learning Between Related Tasks Using Expected Label Proportions (D19-1)
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| Challenge: | Existing methods of data supervision are limited by labeled training data. |
| Approach: | They propose a method where models are trained based on expected label proportions. |
| Outcome: | The proposed method improves on a sentence-level sentiment predictor and is cumulative with LM-based pretraining. |
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| Challenge: | Existing methods for cross-lingual syntactic analysis have been shown to be effective for low-resource languages. |
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| Challenge: | Multi-task learning and semi-supervised learning are successful paradigms for learning in scenarios with limited labelled data. |
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Transfer Learning for Named-Entity Recognition with Neural Networks (L18-1)
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| Challenge: | Existing approaches to named entity recognition (NER) focus on reducing discrepancy between tokens and tokens, but transfer of valuable label information is often not considered or ignored. |
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| Challenge: | Existing methods for relation extraction assume that text is noisy, but its corresponding labels are clean. |
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Exploring and Predicting Transferability across NLP Tasks (2020.emnlp-main)
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Tu Vu, Tong Wang, Tsendsuren Munkhdalai, Alessandro Sordoni, Adam Trischler, Andrew Mattarella-Micke, Subhransu Maji, Mohit Iyyer
| Challenge: | Recent advances in NLP demonstrate the effectiveness of training large-scale language models and transferring them to downstream tasks. |
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Beyond Black & White: Leveraging Annotator Disagreement via Soft-Label Multi-Task Learning (2021.naacl-main)
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| Challenge: | Prior work shows that disagreement between annotators can be useful in training models. |
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Decoupling Adversarial Training for Fair NLP (2021.findings-acl)
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| Challenge: | Existing work assumes main task labels and protected attributes are available in the dataset, but protected labels are often unavailable or only available in limited numbers. |
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Towards Unifying the Label Space for Aspect- and Sentence-based Sentiment Analysis (2022.findings-acl)
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| Challenge: | Existing methods to train ABSA model are limited by lack of annotated data . a dual-granularity pseudo labeling approach is proposed to solve this problem . |
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From Cross-Task Examples to In-Task Prompts: A Graph-Based Pseudo-Labeling Framework for In-context Learning (2025.findings-emnlp)
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| Challenge: | In-context learning (ICL) enables large language models to perform novel tasks without parameter updates by conditioning on a few input-output examples. |
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