With the exponential growth of online marketplaces and user-generated content
therein, aspect-based sentiment analysis has become more important than ever.
In this work, we critically review a representative sample of the models
published during the past six years through the lens of a practitioner, with an
eye towards deployment in production. First, our rigorous empirical evaluation
reveals poor reproducibility: an average 4-5% drop in test accuracy across the
sample. Second, to further bolster our confidence in empirical evaluation, we
report experiments on two challenging data slices, and observe a consistent
12-55% drop in accuracy. Third, we study the possibility of transfer across
domains and observe that as little as 10-25% of the domain-specific training
dataset, when used in conjunction with datasets from other domains within the
same locale, largely closes the gap between complete cross-domain and complete
in-domain predictive performance. Lastly, we open-source two large-scale
annotated review corpora from a large e-commerce portal in India in order to
aid the study of replicability and transfer, with the hope that it will fuel
further growth of the field.

本文通过实践者的角度，对过去 6 年中公布的代表性模型进行了评估，发现评估结果的再现性较差，并对跨领域数据迁移进行了研究，结果表明较少的领域特定数据集能够与同一地区的其他领域数据集结合使用，从而极大地缩小了完全跨领域和完全领域内预测性能之间的差距，并公开了两个大型的印度电子商务评论语料库以促进领域的进一步发展。