HITSZ - HLT at SIGHAN -2024 dim ABSA Task: Integrating BERT and LLM for C hinese Dimensional Aspect-Based Sentiment Analysis (2024)

Abstract: This paper presents the winning system participating in the ACL 2024 workshop SIGHAN-10 shared task: Chinese dimensional aspect-based sentiment analysis (dimABSA). This task aims to identify four sentiment elements in restaurant reviews: aspect, category, opinion, and sentiment intensity evaluated in valence-arousal dimensions, providing a concise yet fine-grained sentiment description for user opinions. To tackle this task, we introduce a system that integrates BERT and large language models (LLM) to leverage their strengths. First, we explore their performance in entity extraction, relation classification, and intensity prediction. Based on preliminary experiments, we develop an integrated approach to fully utilize their advantages in different scenarios. Our system achieves first place in all subtasks and obtains a 41.7% F1-score in quadruple extraction.

## introduction aspect-based sentiment analysis (absa) is a finegrained problem that aims to recognize aspect-level sentiments and opinions of users @xcite . absa generally involves four fundamental elements: (1) aspect term (a), the mention of the reviewed entity in the text; (2) aspect category (c), a predefined category of the evaluated aspect; existing absa works have typically treated sentiment as coarse-grained polarities, overlooking the complexity of sentiment dimensions. pioneeringly, the sighan-2024 dimabsa task (lee 食物#品质 6.5#5.75 6.5#6. figure 1: illustration of three dimabsa subtasks. @xcite proposes to represent sentiment states as continuous real-valued scores in valence-arousal dimensions, referred to as intensity (i). valence measures the positivity or negativity, and arousal evaluates the degree of emotional activation @xcite . as depicted in figure 1 , dimabsa consists of three subtasks: (1) intensity prediction, predicting the intensity of the given aspect; (2) triplet extraction, extracting the triplets composed of (a, o, i) from the given sentence; (3) quadruple extraction, extracting the quadruples composed of (a, c, o, i) from the given sentence. to tackle these subtasks, we develop a system that integrates bert and large language models (llm), representing two leading paradigms for natural language understanding tasks. specifically, we devise both bert-based and llm-based methods and evaluate them to highlight their respective advantages. the bert-based method employs a pipeline approach that sequentially performs aspect-opinion extraction, pairing and classification, and intensity prediction. we implement three improvements to enhance performance: domain-adaptive pre-training @xcite , negative pairs construction, and removing dropout in intensity prediction. the llm-based method transforms the three subtasks into text generation tasks and then fine-tunes a unified model using a multi-task learning strategy. we craft code-style prompts @xcite to enhance the extraction capabilities of llms and employ qlora @xcite to reduce memory usage during training. through preliminary experiments, we make two observations: (1) in structure extraction (aspectopinion extraction and pairing), the bert-based method outperforms the llm-based method; (2) in intensity prediction, the bert-based method performs better with continuous values, while the llm-based method excels in integer-level predictions. therefore, for subtask 1, we employ the bert-based method. for subtask 2 and 3, we utilize the bert-based method to derive the aspect, category, and opinion, which are then fed into llm to generate integer-level intensity predictions. our contributions are summarized as follows: ## conclusions in this paper, we describe our winning system in the sighan-2024 dimabsa task, which involves identifying fundamental sentiment elements in restaurant reviews: aspect, category, opinion, and intensity. our system integrates bert and llm, utilizing their strengths in entity extraction and intensity prediction across three subtasks. the experimental results not only validate the effectiveness of our methods but also underscore the potential of bert-llm ensemble strategies in advanced sentiment analysis, providing technical insights and a solid foundation for future research.