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Found 4 papers, 3 papers with code
Concept Matching for Low-Resource Classification
We propose a model to tackle classification tasks in the presence of very little training data.
Soft Token Matching for Interpretable Low-Resource Classification
We propose a model to tackle classification tasks in the presence of very little training data.
Misspelling Oblivious Word Embeddings
In this paper we present a method to learn word embeddings that are resilient to misspellings.
Deep Character-Level Click-Through Rate Prediction for Sponsored Search
Finally, by combining the predictions of the deep models introduced in this study with the prediction of the model in production of the same commercial search engine, we significantly improve the accuracy and the calibration of the click-through rate prediction of the production system.
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