Abstract
A major challenge for statistical machine translation (SMT) of Arabic-to-English user-generated text is the prevalence of text written in Arabizi, or Romanized Arabic. When facing such texts, a translation system trained on conventional Arabic-English data will suffer from extremely low model coverage. In addition, Arabizi is not regulated by any official standardization and therefore highly ambiguous, which prevents rule-based approaches from achieving good translation results. In this paper, we improve Arabizi-to-English machine translation by presenting a simple but effective Arabizi-to-Arabic transliteration pipeline that does not require knowledge by experts or native Arabic speakers. We incorporate this pipeline into a phrase-based SMT system, and show that translation quality after automatically transliterating Arabizi to Arabic yields results that are comparable to those achieved after human transliteration.- Anthology ID:
- W16-3908
- Volume:
- Proceedings of the 2nd Workshop on Noisy User-generated Text (WNUT)
- Month:
- December
- Year:
- 2016
- Address:
- Osaka, Japan
- Venue:
- WNUT
- SIG:
- Publisher:
- The COLING 2016 Organizing Committee
- Note:
- Pages:
- 43–50
- Language:
- URL:
- https://aclanthology.org/W16-3908
- DOI:
- Cite (ACL):
- Marlies van der Wees, Arianna Bisazza, and Christof Monz. 2016. A Simple but Effective Approach to Improve Arabizi-to-English Statistical Machine Translation. In Proceedings of the 2nd Workshop on Noisy User-generated Text (WNUT), pages 43–50, Osaka, Japan. The COLING 2016 Organizing Committee.
- Cite (Informal):
- A Simple but Effective Approach to Improve Arabizi-to-English Statistical Machine Translation (van der Wees et al., WNUT 2016)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/W16-3908.pdf