| Challenge: | Existing methods for large-scale text classification involve excessive computation and memory overheads. |
| Approach: | They propose a self-supervised and weakly supervised pretraining frameworks for large-scale text classification with multiple categories. |
| Outcome: | The proposed framework improves on the self-supervised and weakly supervised methods while being computationally efficient. |
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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. |
Neural Networks Against (and For) Self-Training: Classification with Small Labeled and Large Unlabeled Sets (2023.findings-acl)
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| Challenge: | Existing models for text classification suffer from the semantic drift problem, which is a problem for self-training. |
| Approach: | They propose a semi-supervised text classifier based on self-training using one positive and one negative property of neural networks. |
| Outcome: | The proposed model outperforms ten baseline models in five benchmarks and is additive to language model pretraining. |
Contextualized Weak Supervision for Text Classification (2020.acl-main)
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| Challenge: | Existing methods for weakly supervised text classification generate pseudo-labels in a context-free manner, thus, the ambiguous, context-dependent nature of human language has been long overlooked. |
| Approach: | They propose a framework that provides contextualized weak supervision for text classification . they leverage contextualized representations of word occurrences and seed word information . |
| Outcome: | The proposed framework provides contextualized weak supervision for text classification . it leverages representations of word occurrences and seed word information to differentiate interpretations . the proposed framework also disambiguates initial seed words, making it fully contextualized . |
Don’t Stop Pretraining: Adapt Language Models to Domains and Tasks (2020.acl-main)
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Suchin Gururangan, Ana Marasović, Swabha Swayamdipta, Kyle Lo, Iz Beltagy, Doug Downey, Noah A. Smith
| Challenge: | Language models prerained on text from a wide variety of sources form the foundation of today’s NLP. |
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Weakly-Supervised Learning of Visual Relations in Multimodal Pretraining (2023.emnlp-main)
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| Challenge: | Recent work in vision-and-language pretraining has investigated supervised signals from object detection data to learn better, fine-grained multimodal representations. |
| Approach: | They propose two approaches to contextualise visual entities in a multimodal setup by using verbalised scene graphs and masked relation prediction. |
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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. |
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Downstream Datasets Make Surprisingly Good Pretraining Corpora (2023.acl-long)
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| Challenge: | a dominant practice is to fine tune large pretrained transformer models using smaller downstream datasets . performance gains are not always attributable to the use of external data in massive amounts . |
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Patton: Language Model Pretraining on Text-Rich Networks (2023.acl-long)
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| Challenge: | Existing models for text-rich networks do not take inter-document structure into account. |
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Lexicon-Enhanced Self-Supervised Training for Multilingual Dense Retrieval (2022.findings-emnlp)
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| Challenge: | Recent multilingual pre-trained models perform poorly on multilingual retrieval tasks due to lack of multilingual training data. |
| Approach: | They propose to mine and generate self-supervised training data based on large-scale unlabeled corpus and introduce query generator to generate more queries in target languages for unlabed passages. |
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How Much Pretraining Does Structured Data Need? (2026.eacl-long)
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| Challenge: | Large language models are increasingly adopted for handling structured data, despite pretraining on unstructured text. |
| Approach: | They propose to re-initialize subsets of layers with random weights before fine-tuning on structured datasets. |
| Outcome: | The proposed models are compared to unstructured datasets and show that they perform well over structured data. |