YNU - HPCC at SIGHAN -2024 dim ABSA Task: Using PLM s with a Joint Learning Strategy for Dimensional Intensity Prediction (2024)

Abstract: The dimensional approach can represent more fine-grained emotional information than discrete affective states. In this paper, a pretrained language model (PLM) with a joint learning strategy is proposed for the SIGHAN-2024 shared task on Chinese dimensional aspect-based sentiment analysis (dimABSA), which requires submitted models to provide fine-grained multi-dimensional (Valance and Arousal) intensity predictions for given aspects of a review. The proposed model consists of three parts: an input layer that concatenates both given aspect terms and input sentences; a Chinese PLM encoder that generates aspect-specific review representation; and separate linear predictors that jointly predict Valence and Arousal sentiment intensities. Moreover, we merge simplified and traditional Chinese training data for data augmentation. Our systems ranked 2nd place out of 5 participants in subtask 1-intensity prediction. The code is publicly available at https://github.com/WZH5127/2024_subtask1_intensity_prediction.

## introduction aspect-based sentiment analysis (absa) @xcite @xcite is used to identify the sentiment polarity regarding specific aspects within a sentence. in recent years, absa tasks have gradually extended into diverse subtasks, including aspect sentiment triplet extraction (aste) @xcite @xcite and aspect sentiment quadruple prediction (asqp) @xcite @xcite . in contrast to these tasks, which consider affective states as discrete classes (positive, neutral, and negative), the dimensional approach provides more finegrained emotional information @xcite . dimensional sentiment analysis represents affective states as continuous numerical values in multi-* corresponding author. ple dimensions, such as valence and arousal space @xcite , as illustrated in figure 1(a) . the valence dimension indicates the degree of positive or negative sentiments, while the arousal dimension refers to the degree of calmness or excitement. valence and arousal are represented by continuous real-valued scores ranging from 1 to 9, with lower scores indicating stronger negative or calm sentiments, higher scores indicating stronger positive or excited sentiments, and mid-range scores, such as 5, indicating neutral states. combining aspect-based and multi-dimensional sentiment analysis, a shared task of chinese dimensional absa shared task (dimabsa) @xcite is proposed in sighan-2024, which primarily includes intensity prediction, triplet extraction, and quadruple extraction. in subtask 1 intensity prediction, given a sentence and an aspect, the system is required to predict the valence and arousal intensities of the sentence regarding the aspect. for instance, in the sentence shown in figure 1(b) , there are two aspects, "柠檬酱"(lemon sauce) and "塔 皮"(tart crust), with required two-dimensional (valence#arousal) intensity predictions of 5.67#5.5 and 4.83#5.0, respective. recently, pre-trained language models (plms) @xcite @xcite have achieved significant success in various natural language processing (nlp) tasks, including sentiment analysis. for instance, using chinese-based plms such as bert-base-chinese and bert-wwm-ext @xcite for intensity prediction on chinese emobank @xcite has yielded superior results compared to traditional methods. however, when these methods tackle dimabsa tasks, they continue to encounter challenges in (1) integrating traditional and simplified chinese for robust review representation and (2) capturing internal relatedness across multiple dimensions for a comprehensive understanding of semantics. to address these issues, we utilized whole-word masking (wwm) @xcite plm of bert-wwm-ext with a joint learning strategy for dimensional intensity prediction in absa. the model consists of the input layer, plm encoder, and dimensional linear layer (dimlinear). initially, we concatenate one aspect term and the review sentence as model sequence input. then, we utilize bert-wwm-ext as the plm encoder to generate robust text representation. finally, the dimlinear layer contains separate linear predictors that jointly predict valence and arousal sentiment intensities. moreover, we merge traditional and simplified chinese training samples into an augmented training set for generalized optimization. in experiments, we found that the integrations between two types of chinese corpora and the joint optimization of multiple dimensions resulted in better performance. consequently, our team ranked 2nd out of 5 participants in subtask 1 of the shared dimabsa task. the remainder of this paper is structured as follows: section 2 describes the architecture of our model in detail. section 3 presents extensive experiments, analysis, and results. finally, conclusions and future work are discussed in section 4. ## system description in this section, we primarily describe the architecture of our model. as depicted in figure 2 , the model comprises three main components: input layer, plm encoder, and dimlinear layer. ## experimental results in this section, we present the comparative results of the proposed methods. ## conclusions this paper presented our system developed for the sighan-2024 shared task dimabsa. our experimental results in subtask 1 demonstrated that our proposed model achieved significant performance in the dimensional intensity prediction of absa. as a result, our team ranked 2nd place in subtask 1. future works will explore incorporating our model with the existing dimensional sentiment analysis corpus and investigating a unified model handling multiple targets in dimabsa.