KIT’s Submission to the IWSLT 2019 Shared Task on Text Translation
In this paper, we describe KIT’s submission for the IWSLT 2019 shared task on text translation. Our system is based on the transformer model [1] using our in-house implementation. We augment the available training data using back-translation and employ fine-tuning for the final model. For our best results, we used a 12-layer transformer-big config- uration, achieving state-of-the-art results on the WMT2018 test set. We also experiment with student-teacher models to improve performance of smaller models.
PDF AbstractTasks
Datasets
Add Datasets
introduced or used in this paper
Results from the Paper
Submit
results from this paper
to get state-of-the-art GitHub badges and help the
community compare results to other papers.
Methods
No methods listed for this paper. Add
relevant methods here
