Adapting Sequence to Sequence models for Text Normalization in Social Media
Table 2: Best performing hyper-parameter settings of our proposed text normalization models
['Hyper-parameter', 'Word-level', 'secondary Char-level', 'S2SChar', 'S2SBPE']
Batch size is set to {32,500}. All of our models are bi-directional and use attention. To compare performance, we tune each model separately with random search. To tune the hyper-parameters we used a 10% random split of the training data and performed random search on the hyper-parameter space. Once the best combination was found, we retrained our system using the full training data set.