CCIIPL ab at SIGHAN -2024 dim ABSA Task: Contrastive Learning-Enhanced Span-based Framework for C hinese Dimensional Aspect-Based Sentiment Analysis (2024)

Abstract: This paper describes our system and findings for SIGHAN-2024 Shared Task Chinese Dimensional Aspect-Based Sentiment Analysis (dimABSA). Our team CCIIPLab proposes an Contrastive Learning-Enhanced Span-based (CL-Span) framework to boost the performance of extracting triplets/quadruples and predicting sentiment intensity. We first employ a span-based framework that integrates contextual representations and incorporates rotary position embedding. This approach fully considers the relational information of entire aspect and opinion terms, and enhancing the model’s understanding of the associations between tokens. Additionally, we utilize contrastive learning to predict sentiment intensities in the valence-arousal dimensions with greater precision. To improve the generalization ability of the model, additional datasets are used to assist training. Experiments have validated the effectiveness of our approach. In the official test results, our system ranked 2nd among the three subtasks.

## introduction aspect-based sentiment analysis (absa) is an important task in natural language processing (nlp), and is beneficial for many downstream tasks, such as emotional conversation generation @xcite and recommendation system @xcite . however, previous work has focused primarily on discrete sentiment polarity, with little attention given to the valence-arousal (va) space. this dimensional approach represents affective states as continuous numerical values across multiple dimensions, providing more fine-grained sentiment information. figure 1: illustration of three dimabsa subtasks. aspect terms, opinion terms and categories are highlighted in red, blue and green, respectively. the terms "val" and "aro" represent the valence and arousal intensity of affective states, respectively, both ranging from 1 to 9. as an extension of the absa task, dimabsa becomes notably challenging due to the following two difficulties: 1) multiple aspect-opinion pairing. in sentences with multiple aspects and opinions, determining which opinion corresponds to which aspect becomes particularly challenging. to mitigate this problem, several efforts @xcite @xcite have been made, but they are not comprehensive for modeling the relationship between tokens, which is significant in the aspect-opinion pairing process @xcite . 2) imbalanced dataset. as illustrated by figure 2 , the number of samples with neutral sentiment intensity is much greater than those with extreme sentiment intensity for both the valence and arousal dimensions, which leads to biased predictions from the model. although there are some methods for addressing data imbalance like re-sampling (rs) @xcite , most of them improve the performance of sparsely labeled samples at the expense of densely labeled samples @xcite , leading to suboptimal results and and cannot be seamlessly migrated to dimabsa tasks. in this paper, we develop a contrastive learning-enhanced span-based framework for the dimabsa task to address the aforementioned challenges. firstly, given the excellent performance of span-based methods in various nlp tasks @xcite , we explicitly generate span representations for all possible aspect and opinion spans. to comprehensively capture the relational information between spans, we integrate the contextual representations and incorporate rotary position embedding (rope) @xcite , which facilitates improved semantic understanding. secondly, as self-supervised learning can improve robustness to data imbalance @xcite , we employ contrastive learning to optimize feature representations in regression tasks. this approach adjusts the dis-tance between samples in the embedding space according to their target values and subsequently leverages this feature to predict sentiment intensity. extensive experiments show that our method performs well across all three subtasks. on the official leaderboard, the mean absolute error (mae) for valence and arousal in subtask 1 ranks 2nd and 1st, respectively. the pearson correlation coefficient (pcc) for valence and arousal in subtask 1 both rank 3rd. the f1 scores for triplet and quadruple extraction in subtasks 2 and 3 both rank 2nd. the paper is structured as follows: section 2 provides a concise review of related work. in section 3, we outline our proposed system. section 4 covers the experimental details, including the dataset, setup, results, and discussions. section 5 analyzes the effectiveness of contrastive learning and further examines the performance of our methods in low-resource settings. finally, section 6 presents a brief conclusion. ## conclusion this paper describes our system for the dimabsa task. we develop a contrastive learning-enhanced span-based framework, which integrates contextual representations and rope into feature representation to enhance semantic understanding. additionally, we employ contrastive learning to optimize feature representations. our system demonstrates significant effectiveness, achieving a 2nd place ranking across three subtasks.