AbstractThe successful application of neural networks to a variety of NLP tasks has provided strong impetus to develop end-to-end models for semantic role labeling which forego the need for extensive feature engineering. Recent approaches rely on high-quality annotations which are costly to obtain, and mostly unavailable in low resource scenarios (e.g., rare languages or domains). Our work aims to reduce the annotation effort involved via semi-supervised learning. We propose an end-to-end SRL model and demonstrate it can effectively leverage unlabeled data under the cross-view training modeling paradigm. Our LSTM-based semantic role labeler is jointly trained with a sentence learner, which performs POS tagging, dependency parsing, and predicate identification which we argue are critical to learning directly from unlabeled data without recourse to external pre-processing tools. Experimental results on the CoNLL-2009 benchmark dataset show that our model outperforms the state of the art in English, and consistently improves performance in other languages, including Chinese, German, and Spanish.