Search Results for author:
Found 3 papers, 1 papers with code
GNOME: Generating Negotiations through Open-Domain Mapping of Exchanges
Our results show that models trained on our dataset not only perform better than previous state of the art models on domain specific strategy prediction, but also generalize better to previously unseen domains.
Combating high variance in Data-Scarce Implicit Hate Speech Classification
Hate speech classification has been a long-standing problem in natural language processing.
Data Agnostic RoBERTa-based Natural Language to SQL Query Generation
Although we have not achieved state of the art results, we have eliminated the need for the table data, right from the training of the model, and have achieved a test set execution accuracy of 76. 7%.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
