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Found 4 papers, 2 papers with code
LAGr: Label Aligned Graphs for Better Systematic Generalization in Semantic Parsing
In this work, we show that better systematic generalization can be achieved by producing the meaning representation directly as a graph and not as a sequence.
LAGr: Labeling Aligned Graphs for Improving Systematic Generalization in Semantic Parsing
In this work, we show that better systematic generalization can be achieved by producing the meaning representation (MR) directly as a graph and not as a sequence.
Exploring the Limits of Few-Shot Link Prediction in Knowledge Graphs
Real-world knowledge graphs are often characterized by low-frequency relations - a challenge that has prompted an increasing interest in few-shot link prediction methods.
On a scalable problem transformation method for multi-label learning
Binary relevance is a simple approach to solve multi-label learning problems where an independent binary classifier is built per each label.
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