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Found 3 papers, 2 papers with code
Unified Embedding Based Personalized Retrieval in Etsy Search
In this paper, we share our novel approach to address both: the semantic gap problem followed by an end to end trained model for personalized semantic retrieval.
Self-Supervised Meta-Learning for Few-Shot Natural Language Classification Tasks
We meta-train a transformer model on this distribution of tasks using a recent meta-learning framework.
Learning to Few-Shot Learn Across Diverse Natural Language Classification Tasks
LEOPARD is trained with the state-of-the-art transformer architecture and shows better generalization to tasks not seen at all during training, with as few as 4 examples per label.
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