UFRGSent at SemEval-2022 Task 10: Structured Sentiment Analysis using a Question Answering Model

Lucas Pessutto, Viviane Moreira


Abstract
This paper describes the system submitted by our team (UFRGSent) to SemEval-2022 Task 10: Structured Sentiment Analysis. We propose a multilingual approach that relies on a Question Answering model to find tuples consisting of aspect, opinion, and holder. The approach starts from general questions and uses the extracted tuple elements to find the remaining components. Finally, we employ an aspect sentiment classification model to classify the polarity of the entire tuple. Despite our method being in a mid-rank position on SemEval competition, we show that the question-answering approach can achieve good coverage retrieving sentiment tuples, allowing room for improvements in the technique.
Anthology ID:
2022.semeval-1.189
Volume:
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
Month:
July
Year:
2022
Address:
Seattle, United States
Editors:
Guy Emerson, Natalie Schluter, Gabriel Stanovsky, Ritesh Kumar, Alexis Palmer, Nathan Schneider, Siddharth Singh, Shyam Ratan
Venue:
SemEval
SIG:
SIGLEX
Publisher:
Association for Computational Linguistics
Note:
Pages:
1360–1365
Language:
URL:
https://aclanthology.org/2022.semeval-1.189
DOI:
10.18653/v1/2022.semeval-1.189
Bibkey:
Cite (ACL):
Lucas Pessutto and Viviane Moreira. 2022. UFRGSent at SemEval-2022 Task 10: Structured Sentiment Analysis using a Question Answering Model. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pages 1360–1365, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
UFRGSent at SemEval-2022 Task 10: Structured Sentiment Analysis using a Question Answering Model (Pessutto & Moreira, SemEval 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.semeval-1.189.pdf
Video:
 https://aclanthology.org/2022.semeval-1.189.mp4
Data
MPQA Opinion Corpus