Cross-Lingual Training for Automatic Question Generation
Table 4: Ablation study showing the importance of both unsupervised and unsupervised pretraining for Hindi
['Model', 'BLEU-1', 'BLEU-2', 'BLEU-3', 'BLEU-4', 'METEOR', 'ROUGE-L']
We construct a model which does not employ any unsupervised or supervised pretraining but uses the same network architecture. This helps in studying the importance of pretraining in our model. We observe that our shared architecture does not directly benefit from the English QG dataset with simple weight sharing. Unsupervised pretraining (with back-translation) helps the shared encoder and decoder layers capture higher-level language-independent information giving an improvement of approximately 7 in BLEU-4 scores. Additionally, the use of parallel data for fine-tuning unsupervised pretraining aids this process further by improving BLEU-4 scores by around 3 points.