Abstract
Quality estimation (QE) for machine translation has emerged as a promising way to provide real-world applications with methods to estimate at run-time the reliability of automatic translations. Real-world applications, however, pose challenges that go beyond those of current QE evaluation settings. For instance, the heterogeneity and the scarce availability of training data might contribute to significantly raise the bar. To address these issues we compare two alternative machine learning paradigms, namely online and multi-task learning, measuring their capability to overcome the limitations of current batch methods. The results of our experiments, which are carried out in the same experimental setting, demonstrate the effectiveness of the two methods and suggest their complementarity. This indicates, as a promising research avenue, the possibility to combine their strengths into an online multi-task approach to the problem.- Anthology ID:
- 2014.amta-workshop.2
- Volume:
- Workshop on interactive and adaptive machine translation
- Month:
- October 22
- Year:
- 2014
- Address:
- Vancouver, Canada
- Venue:
- AMTA
- SIG:
- Publisher:
- Association for Machine Translation in the Americas
- Note:
- Pages:
- 9–19
- Language:
- URL:
- https://aclanthology.org/2014.amta-workshop.2
- DOI:
- Cite (ACL):
- José G.C. de Souza, Marco Turchi, and Matteo Negri. 2014. Towards a combination of online and multitask learning for MT quality estimation: a preliminary study. In Workshop on interactive and adaptive machine translation, pages 9–19, Vancouver, Canada. Association for Machine Translation in the Americas.
- Cite (Informal):
- Towards a combination of online and multitask learning for MT quality estimation: a preliminary study (de Souza et al., AMTA 2014)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/2014.amta-workshop.2.pdf