The Multilingual Microblog Translation Corpus: Improving and Evaluating Translation of User-Generated Text

Paul McNamee, Kevin Duh


Abstract
Translation of the noisy, informal language found in social media has been an understudied problem, with a principal factor being the limited availability of translation corpora in many languages. To address this need we have developed a new corpus containing over 200,000 translations of microblog posts that supports translation of thirteen languages into English. The languages are: Arabic, Chinese, Farsi, French, German, Hindi, Korean, Pashto, Portuguese, Russian, Spanish, Tagalog, and Urdu. We are releasing these data as the Multilingual Microblog Translation Corpus to support futher research in translation of informal language. We establish baselines using this new resource, and we further demonstrate the utility of the corpus by conducting experiments with fine-tuning to improve translation quality from a high performing neural machine translation (NMT) system. Fine-tuning provided substantial gains, ranging from +3.4 to +11.1 BLEU. On average, a relative gain of 21% was observed, demonstrating the utility of the corpus.
Anthology ID:
2022.lrec-1.96
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:
910–918
Language:
URL:
https://aclanthology.org/2022.lrec-1.96
DOI:
Bibkey:
Cite (ACL):
Paul McNamee and Kevin Duh. 2022. The Multilingual Microblog Translation Corpus: Improving and Evaluating Translation of User-Generated Text. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 910–918, Marseille, France. European Language Resources Association.
Cite (Informal):
The Multilingual Microblog Translation Corpus: Improving and Evaluating Translation of User-Generated Text (McNamee & Duh, LREC 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.lrec-1.96.pdf
Data
FLORES-101