  Most existing approaches for zero pronoun resolution are heavily relying on
annotated data, which is often released by shared task organizers. Therefore,
the lack of annotated data becomes a major obstacle in the progress of zero
pronoun resolution task. Also, it is expensive to spend manpower on labeling
the data for better performance. To alleviate the problem above, in this paper,
we propose a simple but novel approach to automatically generate large-scale
pseudo training data for zero pronoun resolution. Furthermore, we successfully
transfer the cloze-style reading comprehension neural network model into zero
pronoun resolution task and propose a two-step training mechanism to overcome
the gap between the pseudo training data and the real one. Experimental results
show that the proposed approach significantly outperforms the state-of-the-art
systems with an absolute improvements of 3.1% F-score on OntoNotes 5.0 data.
