融合内容与对话结构，提升立场检测准确率
Acquired TASTE: Multimodal Stance Detection with Textual and Structural Embeddings
- 用Transformer和无监督结构嵌入联合建模
- 在多个基准上达到当前最优性能
- 适合关注社交语境的自然语言处理研究者
立场检测在话语解析、假新闻传播追踪及科学事实否认识别等下游应用中至关重要。现有模型多依赖话语文本表示，但先前研究已证明对话上下文对立场判断的关键作用。本文提出TASTE——一种融合基于Transformer的内容嵌入与无监督结构嵌入的多模态架构。通过微调预训练Transformer，并利用门控残差网络（GRN）层融合社交嵌入，模型有效捕捉内容与对话结构之间的复杂交互关系。TASTE在多个常用基准上取得当前最优结果，显著优于多种强基线。对比实验验证了社交语境的增益，凸显同时利用内容与结构信息对提升立场检测效果的重要性。
原文摘要 · Abstract (English)
Stance detection plays a pivotal role in enabling an extensive range of downstream applications, from discourse parsing to tracing the spread of fake news and the denial of scientific facts. While most stance classification models rely on textual representation of the utterance in question, prior work has demonstrated the importance of the conversational context in stance detection. In this work we introduce TASTE -- a multimodal architecture for stance detection that harmoniously fuses Transformer-based content embedding with unsupervised structural embedding. Through the fine-tuning of a pretrained transformer and the amalgamation with social embedding via a Gated Residual Network (GRN) layer, our model adeptly captures the complex interplay between content and conversational structure in determining stance. TASTE achieves state-of-the-art results on common benchmarks, significantly outperforming an array of strong baselines. Comparative evaluations underscore the benefits of social grounding -- emphasizing the criticality of concurrently harnessing both content and structure for enhanced stance detection.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。