| Challenge: | Empirically, we show the relative strength of VAMPIRE against computationally expensive contextual embeddings and other popular semi-supervised baselines under low resource settings. |
| Approach: | They propose a lightweight framework for effective text classification when data and computing resources are limited. |
| Outcome: | The proposed framework is compared with expensive contextual embeddings and semi-supervised baselines under low resource settings. |
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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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Delta-training: Simple Semi-Supervised Text Classification using Pretrained Word Embeddings (D19-1)
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| Challenge: | Pretrained word embeddings outperforms classifiers with randomly initialized word embeds, a new method is proposed for semi-supervised text classification. |
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A Structured Variational Autoencoder for Contextual Morphological Inflection (P18-1)
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| Challenge: | morphological inflectors typically trained on fully supervised, type-level data, but how can we improve their performance? et al., 2016: a novel latent-variable model for semi-supervised learning of inflection generation. |
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Virtual Data Augmentation: A Robust and General Framework for Fine-tuning Pre-trained Models (2021.emnlp-main)
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| Challenge: | Recent studies have shown that powerful pre-trained language models can be fooled by small perturbations or intentional attacks. |
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Generative Text Modeling through Short Run Inference (2021.eacl-main)
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| Challenge: | Latent variable models for text capture global semantic and syntactic features when trained correctly. |
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Faithful Low-Resource Data-to-Text Generation through Cycle Training (2023.acl-long)
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| Challenge: | Methods to generate text from structured data have advanced significantly in recent years, but can fail to produce output faithful to the input data, especially on out-of-domain data. |
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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. |
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Train No Evil: Selective Masking for Task-Guided Pre-Training (2020.emnlp-main)
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| Challenge: | Pre-trained language models can't capture domain-specific and task-specific patterns because of the task-agnostic pre-training stage. |
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TADPOLE: Task ADapted Pre-Training via AnOmaLy DEtection (2021.emnlp-main)
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| Challenge: | Existing approaches to solve domain shifts in NLP tasks require additional pre-training . current approaches focus on the downstream corpus when it is small, but are not effective . |
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VarMAE: Pre-training of Variational Masked Autoencoder for Domain-adaptive Language Understanding (2022.findings-emnlp)
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| Challenge: | Pre-trained language models have been widely applied to standard benchmarks due to the limited resources available in a domain. |
| Approach: | They propose a Transformer-based language model called VarMAE for domain-adaptive language understanding that encodes the context of a token into a smooth latent distribution. |
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