Abstract
The increasing role of post-editing as a way of improving machine translation output and a faster alternative to translating from scratch has lately attracted researchers’ attention and various attempts have been proposed to facilitate the task. We experiment with a method to provide support for the post-editing task through error detection. A deep linguistic error analysis was done of a sample of English sentences translated from Portuguese by two Rule-based Machine Translation systems. We designed a set of rules to deal with various systematic translation errors and implemented a subset of these rules covering the errors of tense and number. The evaluation of these rules showed a satisfactory performance. In addition, we performed an experiment with human translators which confirmed that highlighting translation errors during the post-editing can help the translators perform the post-editing task up to 12 seconds per error faster and improve their efficiency by minimizing the number of missed errors.- Anthology ID:
- 2012.amta-wptp.9
- Volume:
- Workshop on Post-Editing Technology and Practice
- Month:
- October 28
- Year:
- 2012
- Address:
- San Diego, California, USA
- Editors:
- Sharon O'Brien, Michel Simard, Lucia Specia
- Venue:
- AMTA
- SIG:
- Publisher:
- Association for Machine Translation in the Americas
- Note:
- Pages:
- Language:
- URL:
- https://aclanthology.org/2012.amta-wptp.9
- DOI:
- Cite (ACL):
- Justina Valotkaite and Munshi Asadullah. 2012. Error Detection for Post-editing Rule-based Machine Translation. In Workshop on Post-Editing Technology and Practice, San Diego, California, USA. Association for Machine Translation in the Americas.
- Cite (Informal):
- Error Detection for Post-editing Rule-based Machine Translation (Valotkaite & Asadullah, AMTA 2012)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-5/2012.amta-wptp.9.pdf