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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 7...52 Hits 41 – 60 of 1.029 41 KOAS: Korean Text Offensiveness Analysis System ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Park, SanHee. - : Underline Science Inc., 2021 BASE Show details 42 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 43 Does Putting a Linguist in the Loop Improve NLU Data Collection ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Parrish, Alicia. - : Underline Science Inc., 2021 BASE Show details 44 What are we learning from language? ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Lupyan, Gary. - : Underline Science Inc., 2021 BASE Show details 45 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 46 Say `YES' to Positivity: Detecting Toxic Language in Workplace Communications ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Awadallah, Ahmed; Bennett, Paul. - : Underline Science Inc., 2021 BASE Show details 47 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 48 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 49 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 50 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 51 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 52 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 53 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 54 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 55 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 56 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 57 Adversarial Examples for Evaluating Math Word Problem Solvers ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Kumar, Vivek; Maheshwary, Rishabh. - : Underline Science Inc., 2021 BASE Show details 58 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 59 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 60 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 7...52

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