Titlebook: Artificial Neural Networks and Machine Learning – ICANN 2022; 31st International C Elias Pimenidis,Plamen Angelov,Mehmet Aydin Conference p

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New Insights into Ovarian Functionowever, there are currently no researchers focusing on KD’s application for relation classification. Although directly leveraging traditional KD methods for relation classification is the easiest way, it should not be neglected that the concept of “relation” is highly ambiguous so machine learning m

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Progesterone Receptors and Ovulationy of the text. An adversarial multi-task learning method is proposed to enhance the modeling and detection ability of character polysemy in Chinese sentence context. Wherein, two models, the masked language model and scoring language model, are introduced as a pair of not only coupled but also adver

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Ursula-F. Habenicht,R. John Aitkenanner. However, unsupervised methods pale by comparison to supervised ones on many tasks. Recently, some unsupervised methods propose to learn sentence representations by maximizing the mutual information between text representations of different levels, such as global MI maximization: global and gl

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Fertility Control — Update and Trends with multi-label classification is the long-tailed distribution of labels. Many studies focus on improving the overall predictions of the model and thus do not prioritise tail-end labels. Improving the tail-end label predictions in multi-label classifications of medical text enables the potential t

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https://doi.org/10.1007/978-4-431-55151-5 a verbalizer which constructs a mapping between label space and label word space, prompt-tuning can achieve excellent results in few-shot scenarios. However, typical prompt-tuning needs a manually designed verbalizer which requires domain expertise and human efforts. And the insufficient label spac

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https://doi.org/10.1007/978-3-031-15931-2artificial intelligence; computational linguistics; computer science; computer systems; computer vision;

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978-3-031-15930-5The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl

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Artificial Neural Networks and Machine Learning – ICANN 2022978-3-031-15931-2Series ISSN 0302-9743 Series E-ISSN 1611-3349