Domain adaptation is an important and widely studied problem in natural
language processing. A large body of literature tries to solve this problem by
adapting models trained on the source domain to the target domain. In this
paper, we instead solve this problem from a dataset perspective. We modify the
source domain dataset with simple lexical transformations to reduce the domain
shift between the source dataset distribution and the target dataset
distribution. We find that models trained on the transformed source domain
dataset performs significantly better than zero-shot models. Using our proposed
transformations to convert standard English to tweets, we reach an unsupervised
part-of-speech (POS) tagging accuracy of 92.14% (from 81.54% zero shot
accuracy), which is only slightly below the supervised performance of 94.45%.
We also use our proposed transformations to synthetically generate tweets and
augment the Twitter dataset to achieve state-of-the-art performance for POS
tagging.

通过简单的词汇转换来减少源域数据集与目标域数据集之间的领域转变，使得模型在变换后的源域数据集上表现明显优于零样本模型，在转换为推特的英文上达到了 92.14% 的无监督词性标注准确率，仅略低于 94.45% 的监督效果，同时利用提出的转换方法来合成推特并增强推特数据集的表现达到了最先进的词性标注性能。

基于词汇转换和标签注入的无监督领域自适应 —— 以推特数据为例

Unsupervised Domain Adaptation using Lexical Transformations and Label  Injection for Twitter Data

We demonstrate the effectiveness of multilingual learning for unsupervised
part-of-speech tagging. The central assumption of our work is that by combining
cues from multiple languages, the structure of each becomes more apparent. We
consider two ways of applying this intuition to the problem of unsupervised
part-of-speech tagging: a model that directly merges tag structures for a pair
of languages into a single sequence and a second model which instead
incorporates multilingual context using latent variables. Both approaches are
formulated as hierarchical Bayesian models, using Markov Chain Monte Carlo
sampling techniques for inference. Our results demonstrate that by
incorporating multilingual evidence we can achieve impressive performance gains
across a range of scenarios. We also found that performance improves steadily
as the number of available languages increases.

通过多语言学习，并运用层次贝叶斯模型和马尔可夫蒙特卡洛采样技术，我们证明了在无监督词性标注中应用多语言学习的有效性，且在可用语言数量增加时，性能稳定提升。