Hitachi at S em E val-2022 Task 10: Comparing Graph- and S eq2 S eq-based Models Highlights Difficulty in Structured Sentiment Analysis (2022)

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.

## models this section explains the graph and seq2seq methods used in this work. ## related work and state-of-the-art sentiment analysis, such as aspect-based sentiment analysis (chen and qian, 2020), is a popular research area in natural language processing. most recently, we have seen attention focusing on parsing full representations of sentiment from text, i.e., structured sentiment analysis @xcite , as we have tackled in this study. most methods @xcite for this task are motivated by graph-based parsers that can parse the structured sentiment using a technique similar to dependency parsing. on the other hand, the state-of-the-art graph-based parser, perin @xcite , utilizes the idea of meaning representation parsing @xcite , which remedies the lossy dependency graphs of the previous work @xcite . our graph-based method (i.e., graph) is one such study. the differences between graph and the related literature are in the graph encoding method and model architecture. to directly compare our graph with the stateof-the-art methods, we trained graph with xlm-roberta-base (xlm-r; @xcite ). we tried three different seeds and averaged the scores. because the corpora provided in the shared task were slightly modified from those used in the related studies, it is difficult to make a direct comparison. thus, we evaluated only on the unaffected norec. on the other hand, an alternative to the traditional graph-based methods, we proposed a generation-based method (i.e., seq2seq) that showed promising results. generation-based methods have been recently utilized in meaning representation parsing @xcite ; this framework offers more research options in terms of architecture and graph encodings for structured sentiment analysis. our study can be positioned within this framework. ## conclusion this paper showed two different parsers (i.e., graphbased and seq2seq-based parsers) for semeval-2022 task 10, structured sentiment analysis. the parsers were compared in various aspects such as complexity in structure and semantics. experiments and analyses showed that both parsers output reasonable predictions, but that it is hard to decide which is better. this could be because the deci-sion criteria rely on how the sentiment structure is defined. this makes structured sentiment analysis challenging. to deal with this difficulty, it may be helpful to apply an abstract representation of structured sentiment.