Improving Aspect-Based Sentiment with End-to-End Semantic Role Labeling Model
RANLP (2023)
Abstract
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.
Authors
BibTex
@article{pvribavn2023improving, title={Improving Aspect-Based Sentiment with End-to-End Semantic Role Labeling Model}, author={P{\v{r}}ib{\'a}{\v{n}}, Pavel and Pra{\v{z}}{\'a}k, Ond{\v{r}}ej}, journal={arXiv preprint arXiv:2307.14785}, year={2023} }
