Papers with semi-supervised learning approach
Neural Self-Training through Spaced Repetition (N19-1)
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| Challenge: | Existing methods for self-training rely on predetermined policies to sample unlabeled data. |
| Approach: | They propose a semi-supervised learning approach that uses spaced repetition to dynamically sample informative and diverse unlabeled instances with respect to individual learner and instance characteristics. |
| Outcome: | The proposed model outperforms existing semi-supervised learning approaches on publicly-available datasets. |
Fake News Detection Strategies under Dataset Bias: Using Large-scale Coarse-grained Labels (2026.eacl-srw)
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| Challenge: | Existing datasets differ substantially in content distributions and annotation policies, complicating fair evaluation and generalization assessment. |
| Approach: | They quantitatively analyze dataset bias across multiple public fake news datasets with different annotation granularities, including article-level and publisher-level labels. |
| Outcome: | The proposed approach improves detection performance under in-dataset and cross-data set evaluation settings. |
Sentence Level Temporality Detection using an Implicit Time-sensed Resource (L18-1)
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| Challenge: | Temporal sense detection of any word is an important aspect for detecting temporality at the sentence level. |
| Approach: | They build a temporal resource based on a semi-supervised learning approach . they use past, present, future, neutral and atemporal senses to tag sentences . |
| Outcome: | The proposed resource is based on a semi-supervised learning approach . it is used to tag sentences with past, present and future temporal senses . |
Combining Self-Training and Self-Supervised Learning for Unsupervised Disfluency Detection (2020.emnlp-main)
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| Challenge: | Existing approaches to disfluency detection rely on human annotations, which are expensive to obtain. |
| Approach: | They propose an unsupervised learning paradigm which can work with unlabeled text corpora. |
| Outcome: | The proposed method performs better than existing supervised systems using word embeddings. |
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. |
Multimodal Semi-supervised Learning for Disaster Tweet Classification (2022.coling-1)
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| Challenge: | During natural disasters, people use social media platforms to post information about casualties and damage . annotating data can be burdensome, subjective and expensive . et al., 2018b; sohn e.t., 2020) proposed semi-supervised multimodal approach to improve performance on multimodal tasks. |
| Approach: | They propose a semi-supervised approach to annotate unlabeled data from Twitter . they extend FixMatch algorithm to a multimodal setting to account for subjective data . |
| Outcome: | The proposed approach improves on multimodal disaster tweet classification tasks. |