  One critical issue of zero anaphora resolution (ZAR) is the scarcity of
labeled data. This study explores how effectively this problem can be
alleviated by data augmentation. We adopt a state-of-the-art data augmentation
method, called the contextual data augmentation (CDA), that generates labeled
training instances using a pretrained language model. The CDA has been reported
to work well for several other natural language processing tasks, including
text classification and machine translation. This study addresses two
underexplored issues on CDA, that is, how to reduce the computational cost of
data augmentation and how to ensure the quality of the generated data. We also
propose two methods to adapt CDA to ZAR: [MASK]-based augmentation and
linguistically-controlled masking. Consequently, the experimental results on
Japanese ZAR show that our methods contribute to both the accuracy gain and the
computation cost reduction. Our closer analysis reveals that the proposed
method can improve the quality of the augmented training data when compared to
the conventional CDA.
