ISCAS at S em E val-2022 Task 10: An Extraction-Validation Pipeline for Structured Sentiment Analysis (2022)

Abstract: ISCAS participated in both sub-tasks in SemEval-2022 Task 10: Structured Sentiment competition. We design an extraction-validation pipeline architecture to tackle both monolingual and cross-lingual sub-tasks. Experimental results show the multilingual effectiveness and cross-lingual robustness of our system. Our system is openly released on: https://github.com/luxinyu1/SemEval2022-Task10/ .

## introduction aspect-based sentiment analysis (absa) aims to detect the fine-grained sentiment tendency lying underneath texts. after decades of development, this area has formed a large family of tasks. nevertheless, many of them are too simple or overlap with each other. meanwhile, the popular evaluation resources are limited both in number and linguistic diversity. semeval-2022 task 10 ( @xcite is proposed to unify different sub-tasks in absa and introduces new metrics, new datasets on different languages to better evaluate methods in this area. task 10 challenges its participants to extract opinion quadruple (holder, target, expression, polarity) from texts across english, spanish, basque, catalan and norwegian in monolingual (sub-task 1) or crosslingual (sub-task 2) manners. figure 1 provides two aspect-level annotations in a same sentence. we applied an extraction-validation pipeline system and participated in both sub-task. our system ranked at 10 th in 32 teams on the monolingual task without using extra data, and achieved competitive performance on the cross-lingual task. besides, the proposed pipeline can be employed universally in monolingual and cross-lingual scenarios. ## results in this section, we report the scores on the development and test datasets of two sub-tasks separately. we use sf 1 (sentiment graph f1), sp (sentiment graph precision) and sr (sentiment graph recall) to evaluate the performance of our system. ## conclusion in this paper, we propose a pipeline system for (holder, target, expression, polarity) quadruple extraction in absa, and adopt a verity of pre-trained language models in distinct parts of system. the evaluation results demonstrate the effectiveness and robustness of our system.