Back-Training excels Self-Training at Unsupervised Domain Adaptation of Question Generation and Passage Retrieval

TL;DR

This paper introduces back-training, a novel unsupervised domain adaptation method that outperforms self-training in question generation and passage retrieval tasks by better aligning outputs with noisy inputs.

Contribution

The paper proposes back-training as an alternative to self-training for UDA, demonstrating significant improvements and introducing a new dataset for domain adaptation research.

Findings

Back-training outperforms self-training with a 7.8 BLEU-4 point improvement.

Back-training achieves 17.6% higher top-20 retrieval accuracy.

Proposed consistency filters improve synthetic data quality.

Abstract

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.