Search Results for author:
Found 2 papers, 0 papers with code
Understanding Advertisements with BERT
This achieves an accuracy of 89. 69{\%}, which is an improvement of 4. 7{\%}.
From Monolingual to Multilingual FAQ Assistant using Multilingual Co-training
We observe that the proposed approach provides consistent gains in the performance of BERT for multiple benchmark datasets (e. g. 1. 0{\%} gain on MLDocs, and 1. 2{\%} gain on XNLI over translate-train with BERT), while requiring a single model for multiple languages.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
