A Study on the Construction of a Large-Scale Multimodal Parallel Corpus of Typical Official Documents of China
Corpora are essential resources for language research, playing a significant role in linguistics, translation studies, and interdisciplinary research. However, current international corpus construction primarily focuses on European and American languages, with insufficient attention to Chinese. Despite notable progress domestically, gaps remain in the development of multimodal bilingual parallel corpora. This study proposes a research plan guided by to construct a large-scale diachronic multimodal bilingual parallel corpus. The corpus will include key official speeches, governance documents, and multimodal data (texts, audio, videos, etc.), with detailed metadata annotation and high-quality bilingual alignment. It aims to support complex retrieval functions and interdisciplinary applications, providing theoretical and data support for linguistics, translation teaching, natural language processing, and international communication. By integrating theories of parallel and multimodal corpora, the study emphasizes corpus balance and representativeness and leverages advanced retrieval platforms for diversified functionality. The corpus demonstrates extensive application value in language teaching, translation practice, and international communication, promoting the standardization and global dissemination of Chinese-specific terms and supporting cultural research. As data expands and technologies improve, the corpus will further strengthen China's cultural confidence and international discourse power in the new era.
Authors
- Liang Xia (ORCID: https://orcid.org/0000-0002-5439-3773)
- Hua Zhang (ORCID: https://orcid.org/0000-0001-9518-740X)
Institutions
- Jinan Maternity And Care Hospital (CN)
Publication Details
- Journal
- Philosophy and social science.
- Published
- 2024-10-01
- DOI
- https://doi.org/10.62381/p243a12
- Citations
- 1
- Primary Topic
- Translation Studies and Practices
- Type
- article
- Field-Weighted Citation Impact
- 2.78