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

CHOIR

熱情的我

,A Novel Approach to?Train Diverse Types of?Language Models for?Health Mention Classification of?Twe that adding noise at earlier layers improves models’ performance whereas adding noise at intermediate layers deteriorates models’ performance. Finally, adding noise towards the final layers performs better than the middle layers noise addition.

SNEER

,Adaptive Knowledge Distillation for?Efficient Relation Classification,od called logit replacement, which can adaptively fix teachers’ mistakes to avoid genetic errors. We conducted comprehensive experiments on the basis of the SemEval-2010 Task 8 relation classification benchmark. Test results demonstrate the effectiveness of the proposed methods.

字形刻痕

,An Unsupervised Sentence Embedding Method by?Maximizing the?Mutual Information of?Augmented Text Reobal MI maximization as well as supervised ones. In this paper, we propose an unsupervised sentence embedding method by maximizing the mutual information of augmented text representations. Experimental results show that our model achieves an average of 73.36% Spearman’s correlation on a series of se

Diatribe

,Chinese Named Entity Recognition Using the?Improved Transformer Encoder and?the?Lexicon Adapter,the position embedding and the self-attention calculation method in the Transformer encoder. Finally, we propose a new architecture of Chinese NER using the improved Transformer encoder and the lexicon adapter. On the four datasets of the Chinese NER task, our model achieves better performance than

想象

Concatenating BioMed-Transformers to Tackle Long Medical Documents and to Improve the Prediction ofxt and multi-sourced electronic health records (EHRs), a challenging task for standard transformers designed to work on short input sequences. A vital contribution of this research is new state-of-the-art (SOTA) results obtained using TransformerXL for predicting medical codes. A variety of experime

unstable-angina

,Eliciting Knowledge from?Pretrained Language Models for?Prototypical Prompt Verbalizer,lem of random initialization of parameters in zero-shot settings, we elicit knowledge from pretrained language models to form initial prototypical embeddings. Our method optimizes models by contrastive learning. Extensive experimental results on several many-class text classification datasets with l

出價(jià)

Lecture Notes in Computer Sciencehttp://image.papertrans.cn/b/image/162658.jpg

紅腫

Ancestor

https://doi.org/10.1007/978-3-642-91296-2 can better deal with dynamic environment and thus make optimal decisions. However, restricted by the limited communication channel, agents have to leverage less communication resources to transmit more informative messages. In this article, we propose a two-level hierarchical multi-agent reinforcem