  Novel neural models have been proposed in recent years for learning under
domain shift. Most models, however, only evaluate on a single task, on
proprietary datasets, or compare to weak baselines, which makes comparison of
models difficult. In this paper, we re-evaluate classic general-purpose
bootstrapping approaches in the context of neural networks under domain shifts
vs. recent neural approaches and propose a novel multi-task tri-training method
that reduces the time and space complexity of classic tri-training. Extensive
experiments on two benchmarks are negative: while our novel method establishes
a new state-of-the-art for sentiment analysis, it does not fare consistently
the best. More importantly, we arrive at the somewhat surprising conclusion
that classic tri-training, with some additions, outperforms the state of the
art. We conclude that classic approaches constitute an important and strong
baseline.
