FGraDA: A Dataset and Benchmark for Fine-Grained Domain Adaptation in Machine Translation

Wenhao Zhu, Shujian Huang, Tong Pu, Pingxuan Huang, Xu Zhang, Jian Yu, Wei Chen, Yanfeng Wang, Jiajun Chen


Abstract
Previous research for adapting a general neural machine translation (NMT) model into a specific domain usually neglects the diversity in translation within the same domain, which is a core problem for domain adaptation in real-world scenarios. One representative of such challenging scenarios is to deploy a translation system for a conference with a specific topic, e.g., global warming or coronavirus, where there are usually extremely less resources due to the limited schedule. To motivate wider investigation in such a scenario, we present a real-world fine-grained domain adaptation task in machine translation (FGraDA). The FGraDA dataset consists of Chinese-English translation task for four sub-domains of information technology: autonomous vehicles, AI education, real-time networks, and smart phone. Each sub-domain is equipped with a development set and test set for evaluation purposes. To be closer to reality, FGraDA does not employ any in-domain bilingual training data but provides bilingual dictionaries and wiki knowledge base, which can be easier obtained within a short time. We benchmark the fine-grained domain adaptation task and present in-depth analyses showing that there are still challenging problems to further improve the performance with heterogeneous resources.
Anthology ID:
2022.lrec-1.723
Volume:
Proceedings of the Thirteenth Language Resources and Evaluation Conference
Month:
June
Year:
2022
Address:
Marseille, France
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
6719–6727
Language:
URL:
https://aclanthology.org/2022.lrec-1.723
DOI:
Bibkey:
Cite (ACL):
Wenhao Zhu, Shujian Huang, Tong Pu, Pingxuan Huang, Xu Zhang, Jian Yu, Wei Chen, Yanfeng Wang, and Jiajun Chen. 2022. FGraDA: A Dataset and Benchmark for Fine-Grained Domain Adaptation in Machine Translation. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 6719–6727, Marseille, France. European Language Resources Association.
Cite (Informal):
FGraDA: A Dataset and Benchmark for Fine-Grained Domain Adaptation in Machine Translation (Zhu et al., LREC 2022)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingestion-script-update/2022.lrec-1.723.pdf
Data
FGraDAASPEC