  The topic of aspect-based sentiment analysis (ABSA) has been explored for a
variety of industries, but it still remains much unexplored in finance. The
recent release of data for an open challenge (FiQA) from the companion
proceedings of WWW '18 has provided valuable finance-specific annotations. FiQA
contains high quality labels, but it still lacks data quantity to apply
traditional ABSA deep learning architecture. In this paper, we employ
high-level semantic representations and methods of inductive transfer learning
for NLP. We experiment with extensions of recently developed domain adaptation
methods and target task fine-tuning that significantly improve performance on a
small dataset. Our results show an 8.7% improvement in the F1 score for
classification and an 11% improvement over the MSE for regression on current
state-of-the-art results.
