Automatic Translating between Ancient Chinese and Contemporary Chinese with Limited Aligned Corpora
This addresses the challenge for native speakers in reading ancient Chinese texts, though it is incremental as it builds on existing neural translation methods.
The paper tackles the problem of translating between ancient and contemporary Chinese by proposing an unsupervised algorithm to build sentence-aligned parallel corpora from limited data, achieving 99.4% F1 score for alignment and translation BLEU scores of 26.95 and 36.34 in both directions.
The Chinese language has evolved a lot during the long-term development. Therefore, native speakers now have trouble in reading sentences written in ancient Chinese. In this paper, we propose to build an end-to-end neural model to automatically translate between ancient and contemporary Chinese. However, the existing ancient-contemporary Chinese parallel corpora are not aligned at the sentence level and sentence-aligned corpora are limited, which makes it difficult to train the model. To build the sentence level parallel training data for the model, we propose an unsupervised algorithm that constructs sentence-aligned ancient-contemporary pairs by using the fact that the aligned sentence pair shares many of the tokens. Based on the aligned corpus, we propose an end-to-end neural model with copying mechanism and local attention to translate between ancient and contemporary Chinese. Experiments show that the proposed unsupervised algorithm achieves 99.4% F1 score for sentence alignment, and the translation model achieves 26.95 BLEU from ancient to contemporary, and 36.34 BLEU from contemporary to ancient.