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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 6...43 Hits 21 – 40 of 852 21 Blindness to Modality Helps Entailment Graph Mining ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; ., Mark; Bijl de Vroe, Sander. - : Underline Science Inc., 2021 BASE Show details 22 Efficient-FedRec: Efficient Federated Learning Framework for Privacy-Preserving News Recommendation ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Liu, Ruixuan; Sun, Guangzhong. - : Underline Science Inc., 2021 BASE Show details 23 Characterizing Test Anxiety on Social Media ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Julka, Esha; Kowalishin, Olivia. - : Underline Science Inc., 2021 BASE Show details 24 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 25 Improving Graph-based Sentence Ordering with Iteratively Predicted Pairwise Orderings ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Lai, Shaopeng. - : Underline Science Inc., 2021 BASE Show details 26 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 27 Machine Translation Decoding beyond Beam Search ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Alayrac, Jean-Baptiste; Antonoglou, Ioannis. - : Underline Science Inc., 2021 BASE Show details 28 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 29 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 30 ProtoInfoMax: Prototypical Networks with Mutual Information Maximization for Out-of-Domain Detection ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; ., Vlado; Fang, Meng. - : Underline Science Inc., 2021 BASE Show details 31 Multi-granularity Textual Adversarial Attack with Behavior Cloning ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Chen, Yangyi; su, jin. - : Underline Science Inc., 2021 BASE Show details 32 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 33 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 34 TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; ., Iryna; Reimers, Nils. - : Underline Science Inc., 2021 BASE Show details 35 WebSRC: A Dataset for Web-Based Structural Reading Comprehension ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Chen, Xingyu. - : Underline Science Inc., 2021 BASE Show details 36 Improving Math Word Problems with Pre-trained Knowledge and Hierarchical Reasoning ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Wen, Yingpeng; Xiao, Nong. - : Underline Science Inc., 2021 BASE Show details 37 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 38 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 39 Corpus-based Open-Domain Event Type Induction ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Shen, Jiaming. - : Underline Science Inc., 2021 BASE Show details 40 Learning with Different Amounts of Annotation: From Zero to Many Labels ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; ., Eunsol; Gong, Chengyue; Zhang, Shujian. - : Underline Science Inc., 2021 Abstract: Anthology paper link: https://aclanthology.org/2021.emnlp-main.601/ Abstract: Training NLP systems typically assumes access to annotated data that has a single human label per example. Given imperfect labeling from annotators and inherent ambiguity of language, we hypothesize that single label is not sufficient to learn the spectrum of language interpretation. We explore new annotation distribution schemes, assigning multiple labels per example for a small subset of training examples. Introducing such multi label examples at the cost of annotating fewer examples brings clear gains on natural language inference task and entity typing task, even when we simply first train with a single label data and then fine tune with multi label examples. Extending a MixUp data augmentation framework, we propose a learning algorithm that can learn from training examples with different amount of annotation (with zero, one, or multiple labels). This algorithm efficiently combines signals from uneven training data and brings ... Keyword: Computational Linguistics; Machine Learning; Machine Learning and Data Mining; Natural Language Processing URL: https://underline.io/lecture/37576-learning-with-different-amounts-of-annotation-from-zero-to-many-labels https://dx.doi.org/10.48448/ys77-8923 BASE Hide details Page: 1 2 3 4 5 6...43

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