言外之意：基于多任务修辞语言建模的作者归属研究
Figuratively Speaking: Authorship Attribution via Multi-Task Figurative Language Modeling
AI总结：
本文首次从修辞语言使用角度研究作者归属问题，提出多任务修辞语言模型MFLM同时检测多种修辞特征，并验证其嵌入能提升作者归属性能。
摘要：
在文本中识别修辞语言（Figurative Language, FL）特征对于各种自然语言处理（NLP）任务至关重要，在这些任务中，理解作者意图及其细微差别是成功沟通的关键。同时，使用多种修辞语言形式的特定组合最能准确反映作者的写作风格，而非仅使用单一结构（如仅使用隐喻或反讽）。因此，我们假设修辞语言特征可以在作者归属（Authorship Attribution, AA）任务中发挥重要作用。我们认为本研究是首个基于修辞语言使用的作者归属计算研究。据此，我们提出了一种多任务修辞语言模型（Multi-task Figurative Language Model, MFLM），该模型能够同时学习检测文本中的多种修辞语言特征。通过在多个测试集上的详细评估，我们证明该模型在修辞语言检测方面往往与专门的二元模型表现相当或更优。随后，我们在三个数据集上评估了联合修辞语言特征对作者归属任务的预测能力，观察到通过整合MFLM嵌入可以提升作者归属性能。
英文摘要：
The identification of Figurative Language (FL) features in text is crucial for various Natural Language Processing (NLP) tasks, where understanding of the author's intended meaning and its nuances is key for successful communication. At the same time, the use of a specific blend of various FL forms most accurately reflects a writer's style, rather than the use of any single construct, such as just metaphors or irony. Thus, we postulate that FL features could play an important role in Authorship Attribution (AA) tasks. We believe that our is the first computational study of AA based on FL use. Accordingly, we propose a Multi-task Figurative Language Model (MFLM) that learns to detect multiple FL features in text at once. We demonstrate, through detailed evaluation across multiple test sets, that the our model tends to perform equally or outperform specialized binary models in FL detection. Subsequently, we evaluate the predictive capability of joint FL features towards the AA task on three datasets, observing improved AA performance through the integration of MFLM embeddings.
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。
