Learning Sentence Embeddings for Coherence Modelling and Beyond
Table 1: The neural sentence position model hyperparameters used in our coherence experiments. The following settings are used across all tasks: batch size of 32, sentence trimming/padding to a length of 25 words, the vocabulary is set to the 1000 most frequent words in the associated training set. The Adamax optimizer is used Kingma and Ba (2014) with default parameters supplied by Keras Chollet et al. (2015).
['Task', 'Dataset', 'Q', 'Epochs', 'Layer dropouts', 'Layer widths']
Training and tuning. Hyperparameter tuning for both tasks is done with a random search, choosing the hyperparameter set with the best validation score averaged across the 10 folds for order discrimination dataset and for three trials for the sentence reordering task.