一种基于跨语言的语义角色标注新方法
A New Method for Cross-Lingual-based Semantic Role Labeling
AI总结：
提出基于模型迁移的深度学习算法，利用英语和波斯语少量数据训练，在单语和跨语言语义角色标注任务上显著超越对比模型，有效缓解多语言标注数据稀缺问题。
摘要：
语义角色标注是自然语言处理中的一项关键任务，能够更好地理解自然语言。然而，多种语言中标注数据的缺乏给研究人员带来了挑战。为了解决这一问题，提出了一种基于模型迁移的深度学习算法。该算法利用由CoNLL2009的英语部分和波斯语语义角色语料库组成的数据集。为了优化训练效率，仅使用了每种语言百分之十的教育数据。所提出模型的结果表明，与Niksirt等人的模型相比有显著改进。在单语模式下，所提出模型在F1分数上取得了2.05%的改进，而在跨语言模式下，改进更为显著，达到6.23%。值得注意的是，对比模型仅训练了语义角色标注四个阶段中的两个阶段，并在其余两个阶段使用了黄金数据。这表明所提出模型的实际优势以显著幅度超越了所报告的数字。语义角色标注跨语言方法的发展具有前景，特别是在解决各种语言标注数据稀缺问题方面。这些进展为进一步研究不同语言语境下的自然语言理解与处理铺平了道路。
英文摘要：
Semantic role labeling is a crucial task in natural language processing, enabling better comprehension of natural language. However, the lack of annotated data in multiple languages has posed a challenge for researchers. To address this, a deep learning algorithm based on model transfer has been proposed. The algorithm utilizes a dataset consisting of the English portion of CoNLL2009 and a corpus of semantic roles in Persian. To optimize the efficiency of training, only ten percent of the educational data from each language is used. The results of the proposed model demonstrate significant improvements compared to Niksirt et al.'s model. In monolingual mode, the proposed model achieved a 2.05 percent improvement on F1-score, while in cross-lingual mode, the improvement was even more substantial, reaching 6.23 percent. Worth noting is that the compared model only trained two of the four stages of semantic role labeling and employed golden data for the remaining two stages. This suggests that the actual superiority of the proposed model surpasses the reported numbers by a significant margin. The development of cross-lingual methods for semantic role labeling holds promise, particularly in addressing the scarcity of annotated data for various languages. These advancements pave the way for further research in understanding and processing natural language across different linguistic contexts.
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。
