Abstract
The emergence of neural machine translation techniques has opened up a new era for developing translation systems. However, it requires a very large amount of parallel corpus, which is scarce for many under-resourced languages, e.g., Bangla. In order to develop a corpus, currently, there is a lack of publicly available collaborative system. In this paper, we report an online collaborative system for the development of the parallel corpus. The system is developed for supporting any language, however, we only evaluated for developing Bangla–English parallel corpus. In a task completion evaluation experiment, the system outperforms the widely used offline system, i.e., OmegaT.
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Notes
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TM is a database consisting of source and target language pairs. It is usually stored in the database while translating the text corpus by the translators.
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Fuzzy matching is an approximate matching approach that tries to find a segment of matched translation by matching them with previously translated sentences. The segment can be a phrase or the whole sentence.
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Acknowledgements
We would like to extend our sincere thanks to A S M Humayun Morshed from Daffodil International University and students from Department of English of the same University for helping us with the data collection task. We also would like to thank our anonymous reviewers for their detailed and constructive comments.
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Hasan, M.A., Alam, F., Noori, S.R.H. (2020). A Collaborative Platform to Collect Data for Developing Machine Translation Systems. In: Uddin, M.S., Bansal, J.C. (eds) Proceedings of International Joint Conference on Computational Intelligence. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-13-7564-4_35
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