Aspect Sentiment Triplet Extraction (ASTE) is a burgeoning subtask of
fine-grained sentiment analysis, aiming to extract structured sentiment
triplets from unstructured textual data. Existing approaches to ASTE often
complicate the task with additional structures or external data. In this
research, we propose a novel tagging scheme and employ a contrastive learning
approach to mitigate these challenges. The proposed approach demonstrates
comparable or superior performance in comparison to state-of-the-art
techniques, while featuring a more compact design and reduced computational
overhead. Notably, even in the era of Large Language Models (LLMs), our method
exhibits superior efficacy compared to GPT 3.5 and GPT 4 in a few-shot learning
scenarios. This study also provides valuable insights for the advancement of
ASTE techniques within the paradigm of large language models.

该研究提出了一种新的标签方案，并采用对比学习方法来解决细粒度情感分析中的困难，表现出与现有技术相媲美或优越的性能，具有更紧凑的设计和较低的计算开销，甚至在大语言模型时代，显示出比 GPT 3.5 和 GPT 4 在少样本学习场景下更为有效的方法。此研究还为大语言模型范式中 ASTE 技术的推进提供了宝贵见解。

重新思考 ASTE：一种最简标签方案与对比学习

Rethinking ASTE: A Minimalist Tagging Scheme Alongside Contrastive  Learning

Detecting norm violations in online communities is critical to maintaining
healthy and safe spaces for online discussions. Existing machine learning
approaches often struggle to adapt to the diverse rules and interpretations
across different communities due to the inherent challenges of fine-tuning
models for such context-specific tasks. In this paper, we introduce
Context-aware Prompt-based Learning for Norm Violation Detection (CPL-NoViD), a
novel method that employs prompt-based learning to detect norm violations
across various types of rules. CPL-NoViD outperforms the baseline by
incorporating context through natural language prompts and demonstrates
improved performance across different rule types. Significantly, it not only
excels in cross-rule-type and cross-community norm violation detection but also
exhibits adaptability in few-shot learning scenarios. Most notably, it
establishes a new state-of-the-art in norm violation detection, surpassing
existing benchmarks. Our work highlights the potential of prompt-based learning
for context-sensitive norm violation detection and paves the way for future
research on more adaptable, context-aware models to better support online
community moderators.

本研究提出了一种使用基于提示的学习，用于检测在线社区中的规范违规行为。CPL-NoViD 通过自然语言提示将上下文融合到模型中，展示了对于不同类型的规则具有改进的性能，它不仅在跨规则类型和跨社区规范违反检测方面表现出色，而且在少样本学习场景中也表现出了适应性，取得了超越现有基准的新的最佳表现。