探索语义角色标注中的非动词谓词：挑战与机遇
Exploring Non-Verbal Predicates in Semantic Role Labeling: Challenges and Opportunities
AI总结：
本文针对语义角色标注中非动词谓词研究不足的问题，提出覆盖多谓词类型的PropBank数据集和人工标注挑战集，证明现有系统跨谓词类型迁移能力有限，并指出SRL远未解决，未来需结合其他语义任务改进非动词谓词处理。
摘要：
尽管我们见证了语义角色标注（SRL）领域令人瞩目的进展，该领域的大多数研究都假设大部分谓词是动词。相反，谓词也可以用其他词类表达，例如名词和形容词。然而，与我们通常用来衡量SRL进展的基准相比，非动词谓词在某些现实场景中出现的频率更低——例如报纸标题、对话和推文等。在本文中，我们提出了一个新的PropBank数据集，该数据集广泛覆盖了多种谓词类型。借助该数据集，我们通过实验证明，标准基准并不能准确反映SRL的现状，并且最先进的系统仍然无法在不同谓词类型之间迁移知识。在观察到这些问题之后，我们还提出了一个新的、人工标注的挑战集，旨在赋予动词性、名词性和形容词性谓词-论元结构同等的重要性。我们利用该数据集来研究是否可以利用不同的语言资源来促进知识迁移。总之，我们认为SRL远未“解决”，将其与其他语义任务相结合可能会在未来带来显著的改进，尤其是对于非动词谓词的长尾部分，从而促进对非动词谓词SRL的进一步研究。
英文摘要：
Although we have witnessed impressive progress in Semantic Role Labeling (SRL), most of the research in the area is carried out assuming that the majority of predicates are verbs. Conversely, predicates can also be expressed using other parts of speech, e.g., nouns and adjectives. However, non-verbal predicates appear in the benchmarks we commonly use to measure progress in SRL less frequently than in some real-world settings -- newspaper headlines, dialogues, and tweets, among others. In this paper, we put forward a new PropBank dataset which boasts wide coverage of multiple predicate types. Thanks to it, we demonstrate empirically that standard benchmarks do not provide an accurate picture of the current situation in SRL and that state-of-the-art systems are still incapable of transferring knowledge across different predicate types. Having observed these issues, we also present a novel, manually-annotated challenge set designed to give equal importance to verbal, nominal, and adjectival predicate-argument structures. We use such dataset to investigate whether we can leverage different linguistic resources to promote knowledge transfer. In conclusion, we claim that SRL is far from "solved", and its integration with other semantic tasks might enable significant improvements in the future, especially for the long tail of non-verbal predicates, thereby facilitating further research on SRL for non-verbal predicates.
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。
