  We introduce the task of predicting adverbial presupposition triggers such as
also and again. Solving such a task requires detecting recurring or similar
events in the discourse context, and has applications in natural language
generation tasks such as summarization and dialogue systems. We create two new
datasets for the task, derived from the Penn Treebank and the Annotated English
Gigaword corpora, as well as a novel attention mechanism tailored to this task.
Our attention mechanism augments a baseline recurrent neural network without
the need for additional trainable parameters, minimizing the added
computational cost of our mechanism. We demonstrate that our model
statistically outperforms a number of baselines, including an LSTM-based
language model.
