Time-Aware Ancient Chinese Text Translation and Inference

Ernie Chang, Yow-Ting Shiue, Hui-Syuan Yeh, Vera Demberg


Abstract
In this paper, we aim to address the challenges surrounding the translation of ancient Chinese text: (1) The linguistic gap due to the difference in eras results in translations that are poor in quality, and (2) most translations are missing the contextual information that is often very crucial to understanding the text. To this end, we improve upon past translation techniques by proposing the following: We reframe the task as a multi-label prediction task where the model predicts both the translation and its particular era. We observe that this helps to bridge the linguistic gap as chronological context is also used as auxiliary information. We validate our framework on a parallel corpus annotated with chronology information and show experimentally its efficacy in producing quality translation outputs. We release both the code and the data for future research.
Anthology ID:
2021.lchange-1.1
Volume:
Proceedings of the 2nd International Workshop on Computational Approaches to Historical Language Change 2021
Month:
August
Year:
2021
Address:
Online
Venues:
ACL | IJCNLP | LChange
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1–6
Language:
URL:
https://aclanthology.org/2021.lchange-1.1
DOI:
10.18653/v1/2021.lchange-1.1
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.lchange-1.1.pdf