  Owing to its unique literal and aesthetical characteristics, automatic
generation of Chinese poetry is still challenging in Artificial Intelligence,
which can hardly be straightforwardly realized by end-to-end methods. In this
paper, we propose a novel iterative polishing framework for highly qualified
Chinese poetry generation. In the first stage, an encoder-decoder structure is
utilized to generate a poem draft. Afterwards, our proposed Quality-Aware
Masked Language Model (QAMLM) is employed to polish the draft towards higher
quality in terms of linguistics and literalness. Based on a multi-task learning
scheme, QA-MLM is able to determine whether polishing is needed based on the
poem draft. Furthermore, QAMLM is able to localize improper characters of the
poem draft and substitute with newly predicted ones accordingly. Benefited from
the masked language model structure, QAMLM incorporates global context
information into the polishing process, which can obtain more appropriate
polishing results than the unidirectional sequential decoding. Moreover, the
iterative polishing process will be terminated automatically when QA-MLM
regards the processed poem as a qualified one. Both human and automatic
evaluation have been conducted, and the results demonstrate that our approach
is effective to improve the performance of encoder-decoder structure.
