  Slot filling, a fundamental module of spoken language understanding, often
suffers from insufficient quantity and diversity of training data. To remedy
this, we propose a novel Cluster-to-Cluster generation framework for Data
Augmentation (DA), named C2C-GenDA. It enlarges the training set by
reconstructing existing utterances into alternative expressions while keeping
semantic. Different from previous DA works that reconstruct utterances one by
one independently, C2C-GenDA jointly encodes multiple existing utterances of
the same semantics and simultaneously decodes multiple unseen expressions.
Jointly generating multiple new utterances allows to consider the relations
between generated instances and encourages diversity. Besides, encoding
multiple existing utterances endows C2C with a wider view of existing
expressions, helping to reduce generation that duplicates existing data.
Experiments on ATIS and Snips datasets show that instances augmented by
C2C-GenDA improve slot filling by 7.99 (11.9%) and 5.76 (13.6%) F-scores
respectively, when there are only hundreds of training utterances.
