Hitachi at SemEval-2022 Task 10: Comparing Graph- and Seq2Seq-based Models Highlights Difficulty in Structured Sentiment Analysis
Gaku Morio, Hiroaki Ozaki, Atsuki Yamaguchi, Yasuhiro Sogawa
Abstract
This paper describes our participation in SemEval-2022 Task 10, a structured sentiment analysis. In this task, we have to parse opinions considering both structure- and context-dependent subjective aspects, which is different from typical dependency parsing. Some of the major parser types have recently been used for semantic and syntactic parsing, while it is still unknown which type can capture structured sentiments well due to their subjective aspects. To this end, we compared two different types of state-of-the-art parser, namely graph-based and seq2seq-based. Our in-depth analyses suggest that, even though graph-based parser generally outperforms the seq2seq-based one, with strong pre-trained language models both parsers can essentially output acceptable and reasonable predictions. The analyses highlight that the difficulty derived from subjective aspects in structured sentiment analysis remains an essential challenge.- Anthology ID:
- 2022.semeval-1.188
- 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:
- 1349–1359
- Language:
- URL:
- https://aclanthology.org/2022.semeval-1.188
- DOI:
- 10.18653/v1/2022.semeval-1.188
- Cite (ACL):
- Gaku Morio, Hiroaki Ozaki, Atsuki Yamaguchi, and Yasuhiro Sogawa. 2022. Hitachi at SemEval-2022 Task 10: Comparing Graph- and Seq2Seq-based Models Highlights Difficulty in Structured Sentiment Analysis. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pages 1349–1359, Seattle, United States. Association for Computational Linguistics.
- Cite (Informal):
- Hitachi at SemEval-2022 Task 10: Comparing Graph- and Seq2Seq-based Models Highlights Difficulty in Structured Sentiment Analysis (Morio et al., SemEval 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/2022.semeval-1.188.pdf
- Data
- MPQA Opinion Corpus