Papers by Arun Kejariwal

1 papers
Harmless Transfer Learning for Item Embeddings (2022.findings-naacl)

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Challenge: Existing approaches to learn item embeddings for categorical features are limited by the frequency of items in real-world.
Approach: They propose a method that transfers knowledge from frequent items to rare items by introducing an auxiliary transfer loss.
Outcome: The proposed framework significantly boosts the performance on a variety of NLP and recommendation system tasks.

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