Neural models have shown several state-ofthe-art performances on Semantic Role Labeling (SRL). However, the neural models require an immense amount of semantic-role corpora and are thus not well suited for lowresource languages or domains. The paper proposes a semi-supervised semantic role labeling method that outperforms the state-ofthe-art in limited SRL training corpora. The method is based on explicitly enforcing syntactic constraints by augmenting the training objective with a syntactic-inconsistency loss component and uses SRL-unlabeled instances to train a joint-objective LSTM. On CoNLL-2012 English section, the proposed semi-supervised training with 1%, 10% SRLlabeled data and varying amounts of SRLunlabeled data achieves +1.58, +0.78 F1, respectively, over the pre-trained models that were trained on SOTA architecture with ELMo on the same SRL-labeled data. Additionally, by using the syntactic-inconsistency loss on inference time, the proposed model achieves +3.67, +2.1 F1 over pre-trained model on 1%, 10% SRL-labeled data, respectively.
More recently, REF have proposed a semi-supervised method for constituencybased SRL.
Towards Semi-Supervised Learning for Deep Semantic Role Labeling
ArXiv
ArXiv
Computer Science