Abstract
Machine Translation (MT) is the most interesting and difficult task which has been posed since the beginning of computer history. The highest difficulty which computers had to face with, is the built-in ambiguity of Natural Languages. Formerly, a lot of human-devised rules have been used to disambiguate those ambiguities. Building such a complete rule-set is time-consuming and labor-intensive task whilst it doesn’t cover all the cases. Besides, when the scale of system increases, it is very difficult to control that rule-set. In this paper, we present a new model of learning-based MT (entitled BTL: Bitext-Transfer Learning) that learns from bilingual corpus to extract disambiguating rules. This model has been experimented in English-to-Vietnamese MT system (EVT) and it gave encouraging results.- Anthology ID:
- 2003.mtsummit-papers.12
- Volume:
- Proceedings of Machine Translation Summit IX: Papers
- Month:
- September 23-27
- Year:
- 2003
- Address:
- New Orleans, USA
- Venue:
- MTSummit
- SIG:
- Publisher:
- Note:
- Pages:
- Language:
- URL:
- https://aclanthology.org/2003.mtsummit-papers.12
- DOI:
- Cite (ACL):
- Dinh Dien, Kiem Hoang, and Eduard Hovy. 2003. BTL: a hybrid model for English-Vietnamese machine translation. In Proceedings of Machine Translation Summit IX: Papers, New Orleans, USA.
- Cite (Informal):
- BTL: a hybrid model for English-Vietnamese machine translation (Dien et al., MTSummit 2003)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2003.mtsummit-papers.12.pdf