@inproceedings{morio-etal-2022-hitachi,
title = "Hitachi at {S}em{E}val-2022 Task 10: Comparing Graph- and {S}eq2{S}eq-based Models Highlights Difficulty in Structured Sentiment Analysis",
author = "Morio, Gaku and
Ozaki, Hiroaki and
Yamaguchi, Atsuki and
Sogawa, Yasuhiro",
editor = "Emerson, Guy and
Schluter, Natalie and
Stanovsky, Gabriel and
Kumar, Ritesh and
Palmer, Alexis and
Schneider, Nathan and
Singh, Siddharth and
Ratan, Shyam",
booktitle = "Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.semeval-1.188",
doi = "10.18653/v1/2022.semeval-1.188",
pages = "1349--1359",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Hitachi at SemEval-2022 Task 10: Comparing Graph- and Seq2Seq-based Models Highlights Difficulty in Structured Sentiment Analysis
%A Morio, Gaku
%A Ozaki, Hiroaki
%A Yamaguchi, Atsuki
%A Sogawa, Yasuhiro
%Y Emerson, Guy
%Y Schluter, Natalie
%Y Stanovsky, Gabriel
%Y Kumar, Ritesh
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y Singh, Siddharth
%Y Ratan, Shyam
%S Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F morio-etal-2022-hitachi
%X 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.
%R 10.18653/v1/2022.semeval-1.188
%U https://aclanthology.org/2022.semeval-1.188
%U https://doi.org/10.18653/v1/2022.semeval-1.188
%P 1349-1359
Markdown (Informal)
[Hitachi at SemEval-2022 Task 10: Comparing Graph- and Seq2Seq-based Models Highlights Difficulty in Structured Sentiment Analysis](https://aclanthology.org/2022.semeval-1.188) (Morio et al., SemEval 2022)
ACL