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Found 4 papers, 0 papers with code
HiGen: Hierarchy-Aware Sequence Generation for Hierarchical Text Classification
The best-performing models aim to learn a static representation by combining document and hierarchical label information.
NGAME: Negative Mining-aware Mini-batching for Extreme Classification
This paper identifies that memory overheads of popular negative mining techniques often force mini-batch sizes to remain small and slow training down.
Neural Models for Output-Space Invariance in Combinatorial Problems
One drawback of the proposed architectures, which are often based on Graph Neural Networks (GNN), is that they cannot generalize across the size of the output space from which variables are assigned a value, for example, set of colors in a GCP, or board-size in sudoku.
Exploring Semi-Supervised Learning for Predicting Listener Backchannels
To analyze our identification module's feasibility, we compared the backchannel prediction models trained on (a) manually-annotated and (b) semi-supervised labels.
