Improving Aspect-Based Sentiment with End-to-End Semantic Role Labeling
  Model
Pavel Přibáň Ondřej Pražák
This paper presents a series of approaches aimed at enhancing the performance
of Aspect-Based Sentiment Analysis (ABSA) by utilizing extracted semantic
information from a Semantic Role Labeling (SRL) model. We propose a novel
end-to-end Semantic Role Labeling model that effectively captures most of the
structured semantic information within the Transformer hidden state. We believe
that this end-to-end model is well-suited for our newly proposed models that
incorporate semantic information. We evaluate the proposed models in two
languages, English and Czech, employing ELECTRA-small models. Our combined
models improve ABSA performance in both languages. Moreover, we achieved new
state-of-the-art results on the Czech ABSA.