PETCI: A Parallel English Translation Dataset of Chinese Idioms
This work addresses a specific challenge in machine translation for Chinese language learners and systems, but it is incremental as it focuses on dataset creation and baseline evaluation.
The authors tackled the problem of poor machine translation of Chinese idioms by creating PETCI, a parallel English translation dataset, and found that structure-aware classification models performed well in distinguishing good translations.
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.