Let's do it ``again'': A First Computational Approach to Detecting Adverbial Presupposition Triggers

Andre Cianflone, Yulan Feng, Jad Kabbara, Jackie Chi Kit Cheung

We introduce the novel task of predicting adverbial presupposition triggers, which is useful for natural language generation tasks such as summarization and dialogue systems. We introduce two new corpora, derived from the Penn Treebank and the Annotated English Gigaword dataset and investigate the use of 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 this model statistically outperforms our baselines.