Exploring Transfer Learning and Domain Data Selection for the Biomedical Translation
Noor-e- Hira, Sadaf Abdul Rauf, Kiran Kiani, Ammara Zafar, Raheel Nawaz · 2019
Transfer Learning and Selective data training are two of the many approaches being extensively investigated to improve the quality of Neural Machine Translation systems.This paper presents a series of experiments by applying transfer learning and selective data training for participation in the Bio-medical shared task of WMT19.We have used Information Retrieval to selectively choose related sentences from out-of-domain data and used them as additional training data using transfer learning.We also report the effect of tokenization on translation model performance.