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Efficient-FedRec: Efficient Federated Learning Framework for Privacy-Preserving News Recommendation ...
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Not All Negatives are Equal: Label-Aware Contrastive Loss for Fine-grained Text Classification ...
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24 |
Improving Graph-based Sentence Ordering with Iteratively Predicted Pairwise Orderings ...
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27 |
Unsupervised Multi-View Post-OCR Error Correction With Language Models ...
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28 |
AttentionRank: Unsupervised Keyphrase Extraction using Self and Cross Attentions ...
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ProtoInfoMax: Prototypical Networks with Mutual Information Maximization for Out-of-Domain Detection ...
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Multi-granularity Textual Adversarial Attack with Behavior Cloning ...
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31 |
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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33 |
WebSRC: A Dataset for Web-Based Structural Reading Comprehension ...
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34 |
Improving Math Word Problems with Pre-trained Knowledge and Hierarchical Reasoning ...
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35 |
Semantic Categorization of Social Knowledge for Commonsense Question Answering ...
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36 |
Pre-train or Annotate? Domain Adaptation with a Constrained Budget ...
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38 |
Learning with Different Amounts of Annotation: From Zero to Many Labels ...
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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 ...
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Keyword:
Computational Linguistics; Machine Learning; Machine Learning and Data Mining; Natural Language Processing
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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
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39 |
Extracting Event Temporal Relations via Hyperbolic Geometry ...
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FastIF: Scalable Influence Functions for Efficient Model Interpretation and Debugging ...
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