姿态检测是主题无关的和跨主题的推广吗？--生殖研究
Is Stance Detection Topic-Independent and Cross-topic Generalizable? -- A Reproduction Study
摘要：
跨主题立场检测是自动检测看不见的主题上的立场（赞成，反对或中立）的任务。我们成功地再现了国家的最先进的跨主题的立场检测工作（赖默斯等。例如，2019），并系统分析其重现性。然后，我们的注意力转向这项工作的跨主题方面，以及词汇和社会文化背景方面的主题的特殊性。我们问：在多大程度上是立场检测主题独立和概括跨主题？我们比较了模型在各种看不见的主题上的性能，并发现主题（例如堕胎，克隆），类（例如赞成，反对）以及它们的相互作用影响模型的性能。我们的结论是，调查性能不同的主题，并解决特定主题的词汇和上下文，是未来的途径跨主题的立场检测。
英文摘要：
Cross-topic stance detection is the task to automatically detect stances (pro, against, or neutral) on unseen topics. We successfully reproduce state-of-the-art cross-topic stance detection work (Reimers et. al., 2019), and systematically analyze its reproducibility. Our attention then turns to the cross-topic aspect of this work, and the specificity of topics in terms of vocabulary and socio-cultural context. We ask: To what extent is stance detection topic-independent and generalizable across topics? We compare the model's performance on various unseen topics, and find topic (e.g. abortion, cloning), class (e.g. pro, con), and their interaction affecting the model's performance. We conclude that investigating performance on different topics, and addressing topic-specific vocabulary and context, is a future avenue for cross-topic stance detection.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
