Conference Publication Details
Mandatory Fields
Catarina Cruz Silva, Chao-Hong Liu, Alberto Poncelas and Andy Way
EMNLP 2018 - Third Conference on Machine Translation (WMT18).
Extracting In-domain Training Corpora for Neural Machine Translation Using Data Selection Methods
2018
October
Published
1
()
Optional Fields
224
231
Brussels, Belgium
31-OCT-18
01-NOV-18
Data selection is a process used in selecting a subset of parallel data for the training of machine translation (MT) systems, so that 1) resources for training might be reduced, 2) trained models could perform better than those trained with the whole corpus, and/or 3) trained models are more tailored to specific domains. It has been shown that for statistical MT (SMT), the use of data selection helps improve the MT performance significantly. In this study, we reviewed three data selection approaches for MT, namely Term Frequency– Inverse Document Frequency, Cross-Entropy Difference and Feature Decay Algorithm, and conducted experiments on Neural Machine Translation (NMT) with the selected data using the three approaches. The results showed that for NMT systems, using data selection also improved the performance, though the gain is not as much as for SMT systems.
https://www.aclweb.org/anthology/W18-6323
10.18653/v1/W18-64023
Grant Details
Science Foundation Ireland (SFI)
SFI Research Centres Programme (Grant 13/RC/2106);European Union’s Horizon 2020 Research and Innovation programme under the Marie SkłodowskaCurie Actions (Grant No. 734211).