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Ara-Women-Hate: The first Arabic Hate Speech corpus regarding Women ...
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Towards the Early Detection of Child Predators in Chat Rooms: A BERT-based Approach ...
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STaCK: Sentence Ordering with Temporal Commonsense Knowledge ...
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Searching for an Effective Defender: Benchmarking Defense against Adversarial Word Substitution ...
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Graphine: A Dataset for Graph-aware Terminology Definition Generation ...
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End-to-end style-conditioned poetry generation: What does it take to learn from examples alone? ...
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To what extent do human explanations of model behavior align with actual model behavior? ...
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Time-aware Graph Neural Network for Entity Alignment between Temporal Knowledge Graphs ...
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What’s Hidden in a One-layer Randomly Weighted Transformer? ...
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Pruning Neural Machine Translation for Speed Using Group Lasso ...
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Elementary-Level Math Word Problem Generation using Pre-Trained Transformers ...
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Does External Knowledge Help Explainable Natural Language Inference? Automatic Evaluation vs. Human Ratings ...
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The Low-Resource Double Bind: An Empirical Study of Pruning for Low-Resource Machine Translation ...
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Knowledge Graph Representation Learning using Ordinary Differential Equations ...
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Abstract:
Anthology paper link: https://aclanthology.org/2021.emnlp-main.750/ Abstract: Knowledge Graph Embeddings (KGEs) have shown promising performance on link prediction tasks by mapping the entities and relations from a knowledge graph into a geometric space. The capability of KGEs in preserving graph characteristics including structural aspects and semantics highly depends on the design of their score function, as well as the inherited abilities from the underlying geometry. Many KGEs use the Euclidean geometry which renders them incapable of preserving complex structures and consequently causes wrong inferences by the models. To address this problem, we propose a neuro differential KGE that embeds nodes of a KG on the trajectories of Ordinary Differential Equations (ODEs). To this end, we represent each relation (edge) in a KG as a vector field on several manifolds. We specifically parameterize ODEs by a neural network to represent complex manifolds and complex vector fields on the manifolds. Therefore, the ...
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Keyword:
Computational Linguistics; Machine Learning; Machine Learning and Data Mining; Natural Language Processing; Neural Network
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URL: https://underline.io/lecture/38088-knowledge-graph-representation-learning-using-ordinary-differential-equations https://dx.doi.org/10.48448/np3y-as12
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What Models Know About Their Attackers: Deriving Attacker Information From Latent Representations ...
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Mind the Context: The Impact of Contextualization in Neural Module Networks for Grounding Visual Referring Expressions ...
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ProtoInfoMax: Prototypical Networks with Mutual Information Maximization for Out-of-Domain Detection ...
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