  Idioms are an important language phenomenon in Chinese, but idiom translation
is notoriously hard. Current machine translation models perform poorly on idiom
translation, while idioms are sparse in many translation datasets. We present
PETCI, a parallel English translation dataset of Chinese idioms, aiming to
improve idiom translation by both human and machine. The dataset is built by
leveraging human and machine effort. Baseline generation models show
unsatisfactory abilities to improve translation, but structure-aware
classification models show good performance on distinguishing good
translations. Furthermore, the size of PETCI can be easily increased without
expertise. Overall, PETCI can be helpful to language learners and machine
translation systems.
