  In this work, we introduce back-training, an alternative to self-training for
unsupervised domain adaptation (UDA) from source to target domain. While
self-training generates synthetic training data where natural inputs are
aligned with noisy outputs, back-training results in natural outputs aligned
with noisy inputs. This significantly reduces the gap between the target domain
and synthetic data distribution, and reduces model overfitting to the source
domain. We run UDA experiments on question generation and passage retrieval
from the \textit{Natural Questions} domain to machine learning and biomedical
domains. We find that back-training vastly outperforms self-training by a mean
improvement of 7.8 BLEU-4 points on generation, and 17.6\% top-20 retrieval
accuracy across both domains. We further propose consistency filters to remove
low-quality synthetic data before training. We also release a new
domain-adaptation dataset- \textit{MLQuestions} containing 35K unaligned
questions, 50K unaligned passages, and 3K aligned question-passage pairs.

Computation and Language;Artificial Intelligence;Machine Learning