Kernel-Based Generative Adversarial Networks for Weakly Supervised Learning

Croce, Danilo; Castellucci, Giuseppe; Basili, Roberto

doi:10.1007/978-3-030-35166-3_24

Cited by 4 publications

(1 citation statement)

References 9 publications

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“…In this way, we exploit both the capability of BERT to produce high-quality representations of input texts and to adopt unlabeled material to help the network in generalizing its representations for the final tasks. At the best of our knowledge, using SS-GANs in NLP has been investigated only by (Croce et al, 2019) with the so-called Kernel-based GAN. In that work, authors extend a Kernel-based Deep Architecture (KDA, (Croce et al, 2017)) with an SS-GAN perspective.…”

Section: Introductionmentioning

confidence: 99%

GAN-BERT: Generative Adversarial Learning for Robust Text Classification with a Bunch of Labeled Examples

Croce

Castellucci

Basili

2020

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

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145

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Recent Transformer-based architectures, e.g., BERT, provide impressive results in many Natural Language Processing tasks. However, most of the adopted benchmarks are made of (sometimes hundreds of) thousands of examples. In many real scenarios, obtaining highquality annotated data is expensive and timeconsuming; in contrast, unlabeled examples characterizing the target task can be, in general, easily collected. One promising method to enable semi-supervised learning has been proposed in image processing, based on Semi-Supervised Generative Adversarial Networks. In this paper, we propose GAN-BERT that extends the fine-tuning of BERT-like architectures with unlabeled data in a generative adversarial setting. Experimental results show that the requirement for annotated examples can be drastically reduced (up to only 50-100 annotated examples), still obtaining good performances in several sentence classification tasks.

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Section: Introductionmentioning

confidence: 99%

GAN-BERT: Generative Adversarial Learning for Robust Text Classification with a Bunch of Labeled Examples

Croce

Castellucci

Basili

2020

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Self Cite

145

View full text Add to dashboard Cite

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Deep learning for named entity recognition: a survey

Hu,

Hou,

Liu

2024

Neural Comput & Applic

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A review of semi-supervised learning for text classification

Duarte

Berton

2023

Artif Intell Rev

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A huge amount of data is generated daily leading to big data challenges. One of them is related to text mining, especially text classification. To perform this task we usually need a large set of labeled data that can be expensive, time-consuming, or difficult to be obtained. Considering this scenario semi-supervised learning (SSL), the branch of machine learning concerned with using labeled and unlabeled data has expanded in volume and scope. Since no recent survey exists to overview how SSL has been used in text classification, we aim to fill this gap and present an up-to-date review of SSL for text classification. We retrieve 1794 works from the last 5 years from IEEE Xplore, ACM Digital Library, Science Direct, and Springer. Then, 157 articles were selected to be included in this review. We present the application domain, datasets, and languages employed in the works. The text representations and machine learning algorithms. We also summarize and organize the works following a recent taxonomy of SSL. We analyze the percentage of labeled data used, the evaluation metrics, and obtained results. Lastly, we present some limitations and future trends in the area. We aim to provide researchers and practitioners with an outline of the area as well as useful information for their current research.

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Kernel-Based Generative Adversarial Networks for Weakly Supervised Learning

Cited by 4 publications

References 9 publications

GAN-BERT: Generative Adversarial Learning for Robust Text Classification with a Bunch of Labeled Examples

GAN-BERT: Generative Adversarial Learning for Robust Text Classification with a Bunch of Labeled Examples

Deep learning for named entity recognition: a survey

A review of semi-supervised learning for text classification

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