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Navigating the Kaleidoscope of COVID-19 Misinformation Using Deep Learning ...
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HittER: Hierarchical Transformers for Knowledge Graph Embeddings ...
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HETFORMER: Heterogeneous Transformer with Sparse Attention for Long-Text Extractive Summarization ...
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Not All Negatives are Equal: Label-Aware Contrastive Loss for Fine-grained Text Classification ...
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Unsupervised Multi-View Post-OCR Error Correction With Language Models ...
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AttentionRank: Unsupervised Keyphrase Extraction using Self and Cross Attentions ...
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Automatic Fact-Checking with Document-level Annotations using BERT and Multiple Instance Learning ...
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Towards the Early Detection of Child Predators in Chat Rooms: A BERT-based Approach ...
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Semantic Categorization of Social Knowledge for Commonsense Question Answering ...
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Pre-train or Annotate? Domain Adaptation with a Constrained Budget ...
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Stepmothers are mean and academics are pretentious: What do pretrained language models learn about you? ...
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CLIFF: Contrastive Learning for Improving Faithfulness and Factuality in Abstractive Summarization ...
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Automatic Text Evaluation through the Lens of Wasserstein Barycenters ...
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Combining sentence and table evidence to predict veracity of factual claims using TaPaS and RoBERTa ...
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Meta Distant Transfer Learning for Pre-trained Language Models ...
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Abstract:
Anthology paper link: https://aclanthology.org/2021.emnlp-main.768/ Abstract: With the wide availability of Pre-trained Language Models (PLMs), multi-task fine-tuning across domains has been extensively applied. For tasks related to distant domains with different class label sets, PLMs may memorize non- transferable knowledge for the target domain and suffer from negative transfer. Inspired by meta-learning, we propose the Meta Distant Transfer Learning (Meta-DTL) framework to learn the cross-task knowledge for PLM-based methods. Meta-DTL first employs task representation learning to mine implicit relations among multiple tasks and classes. Based on the results, it trains a PLM-based meta-learner to capture the transferable knowledge across tasks. The weighted maximum entropy regularizers are proposed to make meta-learner more task-agnostic and unbiased. Finally, the meta-learner can be fine-tuned to fit each task with better parameter initialization. We evaluate Meta-DTL using both BERT and ALBERT on seven ...
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Keyword:
Computational Linguistics; Language Models; Machine Learning; Machine Learning and Data Mining; Natural Language Processing
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URL: https://dx.doi.org/10.48448/xp16-y443 https://underline.io/lecture/37379-meta-distant-transfer-learning-for-pre-trained-language-models
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Temporal Adaptation of BERT and Performance on Downstream Document Classification: Insights from Social Media ...
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