%0 Conference Proceedings %T Jointly Predicting Predicates and Arguments in Neural Semantic Role Labeling %A He, Luheng %A Lee, Kenton %A Levy, Omer %A Zettlemoyer, Luke %Y Gurevych, Iryna %Y Miyao, Yusuke %S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) %D 2018 %8 July %I Association for Computational Linguistics %C Melbourne, Australia %F he-etal-2018-jointly %X Recent BIO-tagging-based neural semantic role labeling models are very high performing, but assume gold predicates as part of the input and cannot incorporate span-level features. We propose an end-to-end approach for jointly predicting all predicates, arguments spans, and the relations between them. The model makes independent decisions about what relationship, if any, holds between every possible word-span pair, and learns contextualized span representations that provide rich, shared input features for each decision. Experiments demonstrate that this approach sets a new state of the art on PropBank SRL without gold predicates. %R 10.18653/v1/P18-2058 %U https://aclanthology.org/P18-2058 %U https://doi.org/10.18653/v1/P18-2058 %P 364-369