Semi-Supervised Semantic Role Labeling: Approaching from an Unsupervised Perspective

Towards Semi-Supervised Learning for Deep Semantic Role Labeling

Neural models have shown several state-of-the-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 low-resource …

Semi-Supervised Semantic Role Labeling via Structural Alignment

Dense FixMatch: a simple semi-supervised learning method for pixel-wise prediction tasks

We propose Dense FixMatch, a simple method for online semi-supervised learning of dense and structured prediction tasks combining pseudo-labeling and consistency regularization via strong data augmentation. We enable the…

Facing the most difficult case of Semantic Role Labeling: A collaboration of word embeddings and co-training

We present a successful collaboration of word embeddings and co-training to tackle in the most difficult test case of semantic role labeling: predicting out-of-domain and unseen semantic frames. Despite the fact that co-…

Semi-Supervised Semantic Role Labeling with Cross-View Training

The 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 …