In practical machine learning settings, the data on which a model must make
predictions often come from a different distribution than the data it was
trained on. Here, we investigate the problem of unsupervised multi-source
domain adaptation, where a model is trained on labelled data from multiple
source domains and must make predictions on a domain for which no labelled data
has been seen. Prior work with CNNs and RNNs has demonstrated the benefit of
mixture of experts, where the predictions of multiple domain expert classifiers
are combined; as well as domain adversarial training, to induce a domain
agnostic representation space. Inspired by this, we investigate how such
methods can be effectively applied to large pretrained transformer models. We
find that domain adversarial training has an effect on the learned
representations of these models while having little effect on their
performance, suggesting that large transformer-based models are already
relatively robust across domains. Additionally, we show that mixture of experts
leads to significant performance improvements by comparing several variants of
mixing functions, including one novel mixture based on attention. Finally, we
demonstrate that the predictions of large pretrained transformer based domain
experts are highly homogenous, making it challenging to learn effective
functions for mixing their predictions.

研究了大型预训练转换模型在无监督多源域自适应中的效果，发现领域对抗性训练对其有影响，但对表现影响不大，同时混合专家方法则显著提高了表现并比较了几种混合函数，包括一种基于注意力的新混合方法。