Papers with Semi-supervised learning
Automatic Rule Induction for Efficient Semi-Supervised Learning (2022.findings-emnlp)
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| Challenge: | Existing approaches to generalize from labeled and unlabeled data are difficult to explain and behave unreliably. |
| Approach: | They propose a framework for automatic discovery and integration of symbolic rules into pretrained transformer models by using an attention mechanism. |
| Outcome: | The proposed framework can improve state-of-the-art methods with no manual effort and minimal computational overhead. |
Graph-Based Semi-Supervised Learning for Natural Language Understanding (D19-53)
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| Challenge: | Semi-supervised learning is an efficient method to augment training data from unlabeled data. |
| Approach: | They propose semi-supervised learning models and their inductive variants for NLU and use them to find similar utterances and construct a graph. |
| Outcome: | The proposed model improves the error rate of the model by 5% using publicly available NLU data and models. |
Advancing Semi-Supervised Learning for Automatic Post-Editing: Data-Synthesis by Mask-Infilling with Erroneous Terms (2024.lrec-main)
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| Challenge: | Semi-supervised learning that leverages synthetic data for training has been widely adopted for developing automatic post-editing models due to the lack of training data. |
| Approach: | They propose a method that uses masked tokens to generate a noisy text from a clean text by infilling mangled tokens with erroneous tokens. |
| Outcome: | The proposed method mimics translation errors found in real data and generates a noisy text from a clean text by infilling masked tokens with erroneous tokens. |
Combining Deep Generative Models and Multi-lingual Pretraining for Semi-supervised Document Classification (2021.eacl-main)
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| Challenge: | Semi-supervised learning and multilingual pretraining have been shown to be effective for task-specific labelled data shortages. |
| Approach: | They propose to combine semi-supervised deep generative models and multi-lingual pretraining to form a pipeline for document classification task. |
| Outcome: | The proposed method outperforms state-of-the-art models in low-resource settings across several languages and outperformed existing models in English. |
Unraveling the Dynamics of Semi-Supervised Hate Speech Detection: The Impact of Unlabeled Data Characteristics and Pseudo-Labeling Strategies (2024.findings-eacl)
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| Challenge: | Semi-supervised learning addresses the need for large amounts of labeled training data for state-of-the-art approaches. |
| Approach: | They propose to leverage unlabeled data to reduce the amount of annotated data required for machine learning based hate speech detection by using a semi-supervised approach. |
| Outcome: | The proposed approach reduces the amount of annotated data required by state-of-the-art models by leveraging unlabeled data. |
Progressive Class Semantic Matching for Semi-supervised Text Classification (2022.naacl-main)
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| Challenge: | Recent semi-supervised learning methods have achieved impressive performance . semi-controlled learning can be used to reduce the annotation cost of text classifiers . |
| Approach: | They propose a semi-supervised learning process that builds a standard K-way classifier and a matching network for the input text and the Class Semantic Representation (CSR). |
| Outcome: | The proposed method improves baselines and overall is more stable. |
Rethinking Semi-supervised Learning with Language Models (2023.findings-acl)
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| Challenge: | Semi-supervised learning (SSL) is a popular setting to make use of unlabelled data . Currently, there are two popular approaches to make effective use of the unlabelled datasets . |
| Approach: | They compare semi-supervised learning (SSL) and task-adaptive pre-training (TAPT) they find TAPT is a stronger and more robust SSL learner, even when using just a few hundred unlabelled samples . |
| Outcome: | The proposed methods improve model performance across different NLP tasks and data sizes. |
Leveraging Training Dynamics and Self-Training for Text Classification (2022.findings-emnlp)
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| Challenge: | Semi-supervised learning (SSL) is a promising technique for improving deep learning models when training data is scarce. |
| Approach: | They propose a semi-supervised learning approach that leverages training dynamics of unlabeled data. |
| Outcome: | The proposed method achieves an average increase in F1 score of 3.5% over baselines in low resource settings. |
Learning to Infer from Unlabeled Data: A Semi-supervised Learning Approach for Robust Natural Language Inference (2022.findings-emnlp)
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| Challenge: | Semi-supervised learning (SSL) is a popular technique for reducing the reliance on human annotations for NLI tasks. |
| Approach: | They propose a way to incorporate unlabeled data into semi-supervised learning (SSL) using a conditional language model, they propose to generate hypotheses for unlabed sentences . |
| Outcome: | The proposed framework significantly improves the performance of four NLI datasets in low-resource settings. |
Jointprop: Joint Semi-supervised Learning for Entity and Relation Extraction with Heterogeneous Graph-based Propagation (2023.acl-long)
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| Challenge: | Named Entity Recognition and Relation Extraction are two crucial tasks in Information Extraction. |
| Approach: | They propose a framework for joint semi-supervised entity and relation extraction that captures the global structure information between tasks and exploits interactions within unlabeled data. |
| Outcome: | The proposed framework outperforms state-of-the-art semi-supervised approaches on NER and RE tasks. |