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.

翻译：尽管我们在语义角色标注（SRL）领域见证了显著进展，但该领域大多数研究均以谓词以动词为主这一假设为前提。然而，谓词同样可通过其他词性（如名词和形容词）表达。不过，在我们常用以衡量SRL进展的基准测试中，非动词谓词的出现频率低于某些现实场景（如新闻标题、对话及推文等）。本文提出一个覆盖多种谓词类型的新型PropBank数据集。借此数据集，我们通过实验证明：现有标准基准未能准确反映SRL的当前发展状况，且当前最先进的系统仍无法在不同谓词类型间迁移知识。基于上述发现，我们进一步构建一个全新的人工标注挑战集，赋予动词、名词和形容词谓词-论元结构同等重要性。我们利用该数据集探究如何借助不同语言资源促进知识迁移。结论表明，SRL远未“解决”，其与其他语义任务的融合或将在未来带来显著改进，尤其对于长尾分布的非动词谓词，从而推动非动词谓词SRL的深入研究。