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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Page: 1 2 3 4 5...14 Hits 1 – 20 of 280 1 Hippocampal ensembles represent sequential relationships among an extended sequence of nonspatial events. Shahbaba, Babak; Li, Lingge; Agostinelli, Forest... In: Nature communications, vol 13, iss 1 (2022) BASE Show details 2 Le modèle Transformer: un « couteau suisse » pour le traitement automatique des langues Yvon, François In: Techniques de l'Ingenieur ; https://hal.archives-ouvertes.fr/hal-03619077 ; Techniques de l'Ingenieur, Techniques de l'ingénieur, 2022, ⟨10.51257/a-v1-in195⟩ ; https://www.techniques-ingenieur.fr/base-documentaire/innovation-th10/innovations-en-electronique-et-tic-42257210/transformer-des-reseaux-de-neurones-pour-le-traitement-automatique-des-langues-in195/ (2022) BASE Show details 3 Automatic Error Type Annotation for Arabic ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Belkebir, Riadh; Habash, Nizar. - : Underline Science Inc., 2021 BASE Show details 4 Navigating the Kaleidoscope of COVID-19 Misinformation Using Deep Learning ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Chen, Yuanzhi; Hasan, Mohammad. - : Underline Science Inc., 2021 BASE Show details 5 HittER: Hierarchical Transformers for Knowledge Graph Embeddings ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Chen, Sanxing; Gao, Jianfeng. - : Underline Science Inc., 2021 BASE Show details 6 Detecting Gender Bias using Explainability ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; ., Krithika; Gupta, Gauri. - : Underline Science Inc., 2021 BASE Show details 7 HETFORMER: Heterogeneous Transformer with Sparse Attention for Long-Text Extractive Summarization ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; He, Lifang; LIU, YE. - : Underline Science Inc., 2021 BASE Show details 8 Not All Negatives are Equal: Label-Aware Contrastive Loss for Fine-grained Text Classification ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Ong, Desmond; Suresh, Varsha. - : Underline Science Inc., 2021 BASE Show details 9 Contrastive Code Representation Learning ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Jain, Ajay; Jain, Paras. - : Underline Science Inc., 2021 BASE Show details 10 Unsupervised Multi-View Post-OCR Error Correction With Language Models ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Brenner, Eliot; Broscheit, Samuel. - : Underline Science Inc., 2021 BASE Show details 11 AttentionRank: Unsupervised Keyphrase Extraction using Self and Cross Attentions ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Ding, Haoran; Luo, Xiao. - : Underline Science Inc., 2021 BASE Show details 12 Automatic Fact-Checking with Document-level Annotations using BERT and Multiple Instance Learning ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Park, Joonsuk; Sathe, Aalok. - : Underline Science Inc., 2021 BASE Show details 13 Towards the Early Detection of Child Predators in Chat Rooms: A BERT-based Approach ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Kumbale, Sinchana; Singh, Smriti. - : Underline Science Inc., 2021 BASE Show details 14 Semantic Categorization of Social Knowledge for Commonsense Question Answering ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Wang, Gengyu. - : Underline Science Inc., 2021 BASE Show details 15 Pre-train or Annotate? Domain Adaptation with a Constrained Budget ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Bai, Fan; Ritter, Alan. - : Underline Science Inc., 2021 BASE Show details 16 Stepmothers are mean and academics are pretentious: What do pretrained language models learn about you? ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Choenni, Rochelle; Shutova, Ekaterina. - : Underline Science Inc., 2021 BASE Show details 17 CLIFF: Contrastive Learning for Improving Faithfulness and Factuality in Abstractive Summarization ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Cao, Shuyang; Wang, Lu. - : Underline Science Inc., 2021 BASE Show details 18 Automatic Text Evaluation through the Lens of Wasserstein Barycenters ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Clavel, Chloe; Colombo, Pierre. - : Underline Science Inc., 2021 BASE Show details 19 Combining sentence and table evidence to predict veracity of factual claims using TaPaS and RoBERTa ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Funkquist, Martin. - : Underline Science Inc., 2021 BASE Show details 20 Meta Distant Transfer Learning for Pre-trained Language Models ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Wang, Chengyu. - : Underline Science Inc., 2021 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 ... Keyword: Computational Linguistics; Language Models; Machine Learning; Machine Learning and Data Mining; Natural Language Processing URL: https://dx.doi.org/10.48448/xp16-y443 https://underline.io/lecture/37379-meta-distant-transfer-learning-for-pre-trained-language-models BASE Hide details Page: 1 2 3 4 5...14

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