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Efficient-FedRec: Efficient Federated Learning Framework for Privacy-Preserving News Recommendation ...
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23 |
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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29 |
ProtoInfoMax: Prototypical Networks with Mutual Information Maximization for Out-of-Domain Detection ...
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30 |
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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32 |
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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39 |
Extracting Event Temporal Relations via Hyperbolic Geometry ...
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Abstract:
Anthology paper link: https://aclanthology.org/2021.emnlp-main.636/ Abstract: Detecting events and their evolution through time is a crucial task in natural language understanding. Recent neural approaches to event temporal relation extraction typically map events to embeddings in the Euclidean space and train a classifier to detect temporal relations between event pairs. However, embeddings in the Euclidean space cannot capture richer asymmetric relations such as event temporal relations. We thus propose to embed events into hyperbolic spaces, which are intrinsically oriented at modeling hierarchical structures. We introduce two approaches to encode events and their temporal relations in hyperbolic spaces. One approach leverages hyperbolic embeddings to directly infer event relations through simple geometrical operations. In the second one, we devise an end-to-end architecture composed of hyperbolic neural units tailored for the temporal relation extraction task. Thorough experimental assessments on widely ...
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Keyword:
Computational Linguistics; Information Extraction; Machine Learning; Machine Learning and Data Mining; Natural Language Processing
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URL: https://dx.doi.org/10.48448/z705-je03 https://underline.io/lecture/37328-extracting-event-temporal-relations-via-hyperbolic-geometry
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40 |
FastIF: Scalable Influence Functions for Efficient Model Interpretation and Debugging ...
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